0011.py

import numpy as np
import matplotlib.pyplot as plt
import pylab as pl
from constants import *
from matplotlib import rcParams as rcp
from matplotlib.cm import ScalarMappable
from tqdm import tqdm
from matplotlib import cm
from matplotlib.ticker import LinearLocator

"""0011.py
This script is used to plot the efficient frontier for different cardinality constraints and risk preferences.
"""

rcp['font.family'],rcp['font.serif'] = 'serif', ['Computer Modern Roman']
rcp['text.usetex'] = True
rcp['font.size'], rcp['axes.labelsize'],rcp['axes.titlesize'] = 16, 16,18
rcp['xtick.labelsize'],rcp['ytick.labelsize'] = 14, 14

try:
    nf = np.load("data/0011.npz")
except:
    nf = np.load("data/EfficientFrontierCardinality.npz")

returns, variance = nf["returns"], nf["variance"]
cardinality_EF, risk_pref_vec, obj_val = nf["cardinality_EF"], nf["risk_pref_vec"], nf["obj_val"]
n1 = len(cardinality_EF)
n2 = len(risk_pref_vec)
fig, ax = plt.subplots(1,1, figsize=(7,2))
colors = pl.cm.hsv(risk_pref_vec/max(risk_pref_vec))

# the cardinality_EF index is along the first dimension obj_val[i,:]

for i in tqdm(range(len(cardinality_EF))):
    returns_ = returns[i,:]
    variance_ = variance[i,:]
    obj_val_ = obj_val[i,:]
    
    ax.plot(variance_, returns_)

#### get meshgrid
riskPrefMat, cardinalityMat = np.meshgrid(risk_pref_vec,cardinality_EF)

###### Plot Returns
fig, ax = plt.subplots(subplot_kw={"projection": "3d"})
surf = ax.plot_surface(riskPrefMat,cardinalityMat,returns,cmap=cm.coolwarm,linewidth=0, antialiased=False)
ax.set_title("Returns")
ax.set_xlabel("Risk")
ax.set_ylabel("Carinality")
fig.colorbar(surf, shrink=0.5, aspect=5)
#fig.savefig("img/efficientfrontiercardinality_returns.png")
plt.subplots_adjust(wspace=0.8, hspace=0.7)
fig.savefig("img/png/0011a.png", bbox_inches='tight')
fig.savefig("img/0011a.pdf", bbox_inches='tight')

######## Plot Variance
fig, ax = plt.subplots(subplot_kw={"projection": "3d"})
surf = ax.plot_surface(riskPrefMat,cardinalityMat,variance,cmap=cm.coolwarm,linewidth=0, antialiased=False)
ax.set_title("Variance")
ax.set_xlabel("Risk")
ax.set_ylabel("Carinality")
fig.colorbar(surf, shrink=0.5, aspect=5)
#fig.savefig("img/efficientfrontiercardinality_variance.png")
plt.subplots_adjust(wspace=0.8, hspace=0.7)
fig.savefig("img/png/0011b.png", bbox_inches='tight')
fig.savefig("img/0011b.pdf", bbox_inches='tight')

####### Plot obj value
fig, ax = plt.subplots(subplot_kw={"projection": "3d"})
surf = ax.plot_surface(riskPrefMat,cardinalityMat,obj_val,cmap=cm.coolwarm,linewidth=0, antialiased=False)
ax.set_title("Objective Value")
ax.set_xlabel("Risk")
ax.set_ylabel("Carinality")
fig.colorbar(surf, shrink=0.5, aspect=5)
# fig.savefig("img/efficientfrontiercardinality_objective.png")
plt.subplots_adjust(wspace=0.8, hspace=0.7)
fig.savefig("img/png/0011c.png", bbox_inches='tight')
fig.savefig("img/0011c.pdf", bbox_inches='tight')

plt.show()