def display_object(coordinates):
print("display_object() START ")
# fig = plt.figure(1, figsize=(10, 5), dpi=90)
# ax = fig.add_subplot(111)
fig, ax = plt.subplots(1, figsize=(10, 5))
latitude_start = -90
latitude_end = 90
longitude_start = 0
longitude_end = 360
break_between = 30
break_between_minor = 10
ax.set_xlim(longitude_start, longitude_end)
ax.set_ylim(latitude_start, latitude_end)
ax.set_xticks(np.arange(longitude_start, longitude_end,
break_between_minor),
minor=True)
ax.set_yticks(np.arange(latitude_start, latitude_end, break_between_minor),
minor=True)
ax.set_xticks(np.arange(longitude_start, longitude_end, break_between))
ax.set_yticks(np.arange(latitude_start, latitude_end, break_between))
ax.grid(which='both')
# push grid lines behind the elements
ax.set_axisbelow(True)
for c in coordinates:
#plt.scatter(c[0], c[1], marker='o', s=1)
plt.fill(c[0], c[1])
plt.show()
def draw(self):
if self.needRefresh:
color_intensity = 1. - 0.5 * self.meanDensity()
rgb_val = [color_intensity, color_intensity, color_intensity]
if self.plot_h['meanPatch'] is None:
polygonList = plt.fill(self.vertices_x,
self.vertices_y,
color=rgb_val)
self.plot_h['meanPatch'] = polygonList[0]
else:
self.plot_h['meanPatch'].set_color(rgb_val)
self.needRefresh = 0
def draw_rectangle(center, theta, height, width, color, edge_color, display_option):
x = center(1)
y = center(2)
x_v = [x ,x + height, x + height, x, x]
y_v = [y , y, y + width , y + width, y]
# rotate angle theta
R = []
R[1,:]=x_v - x
R[2,:]=y_v - y
XY = [math.cos(theta) - math.sin(theta), math.sin(theta), math.cos(theta)]*R;
XY[1,:] = XY[1,:] + x
XY[2,:] = XY[2,:] + y
R = rotate(height / 2, width / 2, theta)
X = XY[1,:] - R[1]
Y = XY[2,:] - R[2]
RectLines = [[X[1],Y[1],X[2],Y[2]],[X[2],Y[2],X[3],Y[3]], [X[3],Y[3],X[4],Y[4]],[X[4],Y[4],X[5],Y[5]]]
if (display_option):
rect = matplotlib.fill(X, Y, color)
set(rect, 'FaceColor', color, 'EdgeColor', edge_color, 'LineWidth', 1)
def graph_intervals(
X_test,
y,
best_model,
actual_label,
lower_bound_label,
upper_bound_label,
interval_label,
title,
model_params,
n=100,
# x_axis_col="store_order_id",
):
# Don't show plots.
y_interval_predicted = best_model.predict(X_test)
X = X_test.copy()
pd.options.mode.chained_assignment = None
X[lower_bound_label] = y_interval_predicted[0, :, 0]
X[upper_bound_label] = y_interval_predicted[0, :, 1]
# X[lower_bound_label] = best_model.forest.predict(X_test)
# X[upper_bound_label] = best_model.forest.predict(X_test)
X[actual_label] = y
pd.options.mode.chained_assignment = "warn"
plt.rcParams["figure.figsize"] = [9, 16]
plt.rcParams["figure.dpi"] = 400
fig = plt.figure()
X_sample_w_index = X.sample(n=n).sort_values(by=[actual_label])
X_sample = X_sample_w_index.reset_index()
plt.plot(X_sample.index,
X_sample[actual_label],
"b.",
markersize=10,
label=u"Observations")
plt.plot(X_sample.index, X_sample[lower_bound_label], "k-")
plt.plot(X_sample.index, X_sample[upper_bound_label], "k-")
plt.fill(
np.concatenate([X_sample.index, X_sample.index[::-1]]),
np.concatenate(
[X_sample[upper_bound_label], X_sample[lower_bound_label][::-1]]),
alpha=0.2,
fc="b",
ec="None",
label=interval_label,
)
contains = (len(X[(X[lower_bound_label] <= X[actual_label])
& (X[actual_label] <= X[upper_bound_label])].index) /
len(X.index))
over_estimates = len(
X[(X[lower_bound_label] > X[actual_label])].index) / len(X.index)
under_estimates = len(
X[(X[upper_bound_label] < X[actual_label])].index) / len(X.index)
median_interval_width = (X[upper_bound_label] -
X[lower_bound_label]).median()
score = interval_score(y, y_interval_predicted)
scores = (r"contains={0:.0%}" + "\n" + r"underestimates={1:.0%}" + "\n" +
r"overestimates={2:.0%}" + "\n" + r"med_interval_width={3:.2f}" +
"\n" + r"score={4:.2f}").format(contains, over_estimates,
under_estimates,
median_interval_width, score)
extra = plt.Rectangle((0, 0),
0,
0,
fc="w",
fill=False,
edgecolor="none",
linewidth=0)
plt.legend([extra], [scores], loc="upper left")
plt.title(title + ":" + model_params)
plt_filename = title + ":" + model_params + ".png"
plt.savefig(plt_filename, bbox_inches="tight")
plt.close(fig)
return plt_filename