"""
Created on Dec 20, 2019
@author: @sahin-abdullah
This submodule produces two different figures: (1) emulator vs simulator,
(2) regression lines of first order component functions
"""
import matplotlib.pyplot as plt
import numpy as np
[docs]
def plot(Si):
# Close all figures
plt.close("all")
# Extract necessary data from Si
try:
problem = Si.problem
except AttributeError:
problem = Si
Em = Si["Em"]
RT = Si["RT"]
Y_em = Si["Y_em"]
idx = Si["idx"]
X = Si["X"]
Y = Si["Y"]
# Get number of bootstrap from Runtime and sample size N
K = RT.shape[0]
N = Y.shape[0]
row = 2
col = 5
try:
ax = Si._plot()
except AttributeError:
# basic bar plot not found or is not available
pass
# Plot emulator performance
Y_p = np.linspace(np.min(Y), np.max(Y), N, endpoint=True)
start = max(0, K - 10)
for i in range(start, K):
# Only showing the last 10
if (i % (row * col) == 0) or (i == 0):
fig = plt.figure(frameon=False)
fig.suptitle("Showing last 10 Bootstrap Trials")
it = 1
title_str = f"Trial {K - i}"
ax = fig.add_subplot(row, col, it, frameon=True, title=title_str)
ax.plot(Em["Y_e"][:, i], Y[idx[:, i]], "r+", label="Emulator")
ax.plot(Y_p, Y_p, "darkgray", label="1:1 Line")
ax.axis("square")
ax.set_xlim(np.min(Y), np.max(Y))
ax.set_ylim(np.min(Y), np.max(Y))
it += 1
if i == (K - 1):
handles, labels = ax.get_legend_handles_labels()
fig.legend(handles, labels, loc="center right", bbox_to_anchor=(1.0, 0.5))
fig.tight_layout()
# Now plot regression lines of component functions
row = 3
col = 1
last_bootstrap = idx[:, -1]
for i in range(X.shape[1]):
if (i % (row * col) == 0) or (i == 0):
fig = plt.figure(frameon=False)
it = 1
title_str = (
"Regression of parameter " + problem["names"][i] + r"$^{(Last Trial)}$"
)
ax = fig.add_subplot(row, col, it, frameon=True, title=title_str)
ax.plot(X[last_bootstrap, i], Y[last_bootstrap], "r.")
ax.plot(X[last_bootstrap, i], np.mean(Em["f0"]) + Y_em[:, i], "k.")
ax.legend(
[r"$\widetilde{Y}$", "$f_{" + str(i + 1) + "}$"],
loc="upper left",
bbox_to_anchor=(1.04, 1.0),
)
it += 1
fig.tight_layout()
plt.show()
if "emulated" in Si:
emulated = Si["emulated"]
# Sum of squared residuals
Y_test = Si["Y_test"]
ssr = np.sum((emulated - Y_test) ** 2)
# Plot testing results
fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(6, 6))
ax.plot(emulated, Y_test, "r+", label="Emulator")
ax.plot(Y_test, Y_test, "darkgray", label="1:1 Line")
ax.axis("square")
ax.set_xlabel("Emulator")
ax.set_ylabel("New Observation")
ax.set_xlim(np.min(Y_test), np.max(Y_test))
ax.set_ylim(np.min(Y_test), np.max(Y_test))
ax.legend(loc="lower right")
plt.title(f"Testing results\nSSR = {ssr:.2f}")
fig.tight_layout()
plt.show()
return ax
if __name__ == "__main__":
pass
