def plot_boost_i(pos, i):
plt.subplot(2, nb_subplot, pos + nb_subplot, label=str(i))
style_figs.light_axis()
y_loss = [int(x) for x in np.abs(y - y_pred) * 100]
plt.scatter(X, y, color='grey', edgecolor='k', s=y_loss)
y_pred_i = adaboost[-1].estimators_[i - 1].predict(X)
plt.plot(X, y_pred_i, color='orange')
def plot_bagging3(plot_line=False, new_point=False):
col_sep = 'grey'
# 1
plt.subplot(1, 3, 1)
style_figs.light_axis()
circle = [[1, 1], [1, 3], [1.5, 2], [5, 5.8]]
square = [[3, 1], [3, 4], [3.5, 2], [5, 1]]
plot_bagging(circle, square, new_point=new_point)
if plot_line:
plt.plot([2.3, 2.3], [0, 7], col_sep, ls='--')
# 2
plt.subplot(1, 3, 2)
style_figs.light_axis()
circle = [[.5, 6], [2, 5.6], [4, 5.5], [5, 5.8]]
square = [[3, 1], [3.5, 2], [6.2, 3], [4.8, 2.8]]
plot_bagging(circle, square, new_point=new_point)
if plot_line:
plt.plot([0, 7], [4, 4], col_sep, ls='--')
# 3
plt.subplot(1, 3, 3)
style_figs.light_axis()
circle = [[1, 1], [1.5, 2], [1.4, 4.4], [3, 6.5], [4, 5.5]]
square = [[3, 1], [5, 4.5], [5, 1], [6.2, 3]]
if plot_line:
plt.plot([4.5, 4.5], [0, 7], col_sep, ls='--')
plot_bagging(circle, square, new_point=new_point)
style_figs.light_axis()
# Create linear regression object
regr = linear_model.LinearRegression()
regr.fit(x.reshape((-1, 1)), y)
plt.scatter(x, y, color='k', s=9)
plt.plot([-1, 1], regr.predict([[
-1,
], [
1,
]]), linewidth=3)
plt.axis('tight')
plt.xlim(-1, 1)
ymin, ymax = plt.ylim()
style_figs.light_axis()
plt.ylabel('y', size=16, weight=600)
plt.xlabel('x', size=16, weight=600)
plt.savefig('ols_simple.svg', facecolor='none', edgecolor='none')
plt.scatter(x_test, y_test, color='C1', s=9, zorder=20, alpha=.4)
plt.xlim(-1, 1)
plt.ylim(ymin, ymax)
plt.savefig('ols_simple_test.svg', facecolor='none', edgecolor='none')
# %%
# Plot cubic splines
plt.clf()
ax = plt.axes([.1, .1, .9, .9])
def plot_boost(pos, size=None):
plt.subplot(2, nb_subplot, pos, label=str(i) + str(size))
style_figs.light_axis()
plt.scatter(X, y, color='grey', edgecolor='k', s=size)
plt.plot(X, y_pred, color='blue')
