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generate_images.py
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generate_images.py
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import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from scipy.stats import multivariate_normal
from os.path import dirname, abspath
import datetime as dt
import pickle
import matplotlib.colors as mcolors
colors = list(mcolors.TABLEAU_COLORS.values())
plt.rcParams.update({
"text.usetex": True,
"font.family": "serif"
})
def generate_expected_error_3d_plot():
d = dirname(abspath(__file__))
mu1 = np.array([-1., 2.])
sigma1 = np.array([[1., .5], [.5, 1.]])
mu2 = np.array([2., -1.])
sigma2 = np.array([[1.5, .5], [.5, 1.5]])
mu3 = np.array([-2., -2.])
sigma3 = np.array([[0.5, .2], [.2, 0.5]])
x = np.linspace(-5, 5, 100)
y = np.linspace(-5, 5, 100)
x, y = np.meshgrid(x, y)
rv1 = multivariate_normal(mu1, sigma1)
rv2 = multivariate_normal(mu2, sigma2)
rv3 = multivariate_normal(mu3, sigma3)
z = np.zeros_like(x)
for i in range(len(x)):
for j in range(len(y)):
pos = np.array([x[i, j], y[i, j]])
z[i, j] = rv1.pdf(pos) + rv2.pdf(pos) + rv3.pdf(pos)
z = z / np.sum(z)
pos = np.dstack((x, y))
fig = plt.figure(figsize=(12, 6))
ax1 = fig.add_subplot(121, projection='3d')
ax1.plot_surface(x, y, z, cmap='viridis')
ax1.set_title("Distribution of explanatory features", fontsize=14, fontname='serif')
z2 = 1 - z
z2 = (z2 - np.min(z2)) / (np.max(z2) - np.min(z2))
ax2 = fig.add_subplot(122, projection='3d')
ax2.plot_surface(x, y, z2, cmap='viridis')
ax2.set_title("Expected error of a point forecaster by region", fontsize=14, fontname='serif')
ax2.set_zticks([0, 1])
ax2.set_zticklabels([" Min Error", " Max Error"], fontsize=10)
# plt.show()
plt.savefig(d + "\\Images\\expected_error_3d_plot.pdf")
def generate_day_ahead_market_figure(test_date=dt.datetime(2022, 10, 5)):
d = dirname(abspath(__file__))
df = pd.read_csv(d + "\\Data\\df.csv")
df['full_date'] = pd.to_datetime(df['full_date'])
fig, ax = plt.subplots(1, 1, figsize=(10, 4))
ax.plot(df['full_date'], df['real'])
ax.plot(df.loc[df[df.full_date > test_date].index - 6*30*24, 'full_date'], df.loc[df[df.full_date > test_date].index - 6*30*24, 'real'])
ax.plot(df.loc[df[df.full_date > test_date].index, 'full_date'], df.loc[df[df.full_date > test_date].index, 'real'])
ax.axvline(x=test_date, color='k', linestyle='--', lw=2)
ax.axvline(x=test_date - dt.timedelta(days=6*30), color='k', linestyle='--', lw=2)
ax.grid()
ax.set_title("Spanish Day-Ahead market price", fontsize=16)
ax.set_xlabel("Date", fontsize=12)
ax.set_ylabel("Price (€/MWh)", fontsize=12)
ax.text(test_date - dt.timedelta(days=15), 650, "Quantile Regression predictions", ha='right', va='top', rotation=0, fontsize=11)
ax.annotate("", xy=(test_date - dt.timedelta(days=5), 610), xytext=(test_date - dt.timedelta(days=6*30), 610),
arrowprops=dict(arrowstyle="->"))
ax.text(test_date + dt.timedelta(days=6*30) - dt.timedelta(days=15), 650, "Conformal prediction methods", ha='right', va='top', rotation=0, fontsize=11)
ax.annotate("", xy=(test_date + dt.timedelta(days=6*30), 610), xytext=(test_date, 610),
arrowprops=dict(arrowstyle="->"))
plt.savefig(d + "\\Images\\day_ahead_market_figure.pdf")
plt.tight_layout()
plt.show()
def generate_day_ahead_market_predictions():
d = dirname(abspath(__file__))
df = pd.read_csv(d + "\\Data\\df.csv")
df['full_date'] = pd.to_datetime(df['full_date'])
df_aux = df[(pd.to_datetime(df.date) >= dt.datetime(2022, 3, 5)) & (pd.to_datetime(df.date) < dt.datetime(2022, 3, 5) + dt.timedelta(days=7))]
fig, ax = plt.subplots(1, 1, figsize=(10, 4))
ax.plot(df_aux['full_date'], df_aux['real'])
ax.plot(df_aux['full_date'], df_aux['pred1'], '--', label='Model 1')
ax.plot(df_aux['full_date'], df_aux['pred2'], '--', label='Model 2')
ax.plot(df_aux['full_date'], df_aux['pred3'], '--', label='Model 3')
ax.plot(df_aux['full_date'], df_aux['pred4'], '--', label='Model 4')
for date in df_aux.date.unique():
ax.axvline(x=pd.to_datetime(date), color='k', linestyle='--', lw=1)
ax.grid()
ax.set_title("Spanish Day-Ahead market price forecasts", fontsize=16)
ax.set_xlabel("Date", fontsize=12)
ax.set_ylabel("Price (€/MWh)", fontsize=12)
ax.legend()
plt.tight_layout()
plt.savefig(d + "\\Images\\day_ahead_market_predictions.pdf")
# plt.show()
def generate_progression_alphas(hour=13):
d = dirname(abspath(__file__))
with open('dict_list_alphas_aci.pkl', 'rb') as file:
list_alphas_aci = pickle.load(file)
with open('dict_list_alphas_waci.pkl', 'rb') as file:
list_alphas_waci = pickle.load(file)
list_alphas_aci = list_alphas_aci[hour]
list_alphas_waci = list_alphas_waci[hour]
for i in range(len(list_alphas_aci)):
fig, ax = plt.subplots(1, 1, figsize=(10, 4))
ax.axhline(0.2, xmin= 0, xmax=500, lw=1, color='k', linestyle='--', label='Objective miscoverage', alpha=0.4)
ax.plot(np.arange(0, 500, 0.1), list_alphas_waci[i], color=colors[0], label='WACI', lw=1)
ax.axhline(list_alphas_aci[i], xmin= 0, xmax=500, lw=1, color=colors[1], label='ACI')
ax.set_xlabel('Interval length', fontsize=14)
ax.set_ylabel("$\\alpha_t$", fontsize=14)
ax.grid()
ax.set_ylim(0.1, 0.3)
ax.set_xlim(0, 100)
ax.legend(prop={'size': 14})
ax.tick_params(axis='x', labelsize=12)
ax.tick_params(axis='y', labelsize=12)
plt.tight_layout()
plt.savefig(d + f"\\Images\\alpha_progression\\alpha_progression_hour{hour}_{i}.pdf")
def generate_wind_forecasts():
d = dirname(abspath(__file__))
df = pd.read_csv(d + "\\Data\\df_wind.csv")
df['full_date'] = pd.to_datetime(df['full_date'])
df_aux = df[(pd.to_datetime(df.date) >= dt.datetime(2021, 3, 5)) & (pd.to_datetime(df.date) < dt.datetime(2021, 3, 5) + dt.timedelta(days=7))]
fig, ax = plt.subplots(1, 1, figsize=(10, 4))
ax.plot(df_aux['full_date'], df_aux['real'])
ax.plot(df_aux['full_date'], df_aux['pred1'], '--', label='Model 1')
ax.plot(df_aux['full_date'], df_aux['pred2'], '--', label='Model 2')
ax.plot(df_aux['full_date'], df_aux['pred3'], '--', label='Model 3')
for date in df_aux.date.unique():
ax.axvline(x=pd.to_datetime(date), color='k', linestyle='--', lw=1)
ax.grid()
ax.set_title("Wind power forecasts", fontsize=16)
ax.set_xlabel("Date", fontsize=12)
ax.set_ylabel("GWh", fontsize=12)
ax.legend()
plt.tight_layout()
plt.savefig(d + "\\Images\\wind_energy_forecasts.pdf")
plt.show()
def generate_wind_power_figure(test_date=dt.datetime(2021, 1, 1)):
d = dirname(abspath(__file__))
df = pd.read_csv(d + "\\Data\\df_wind.csv")
df['full_date'] = pd.to_datetime(df['full_date'])
fig, ax = plt.subplots(1, 1, figsize=(10, 4))
ax.plot(df['full_date'], df['real'])
ax.plot(df.loc[df[df.full_date > test_date].index - 6*30*24, 'full_date'], df.loc[df[df.full_date > test_date].index - 6*30*24, 'real'])
ax.plot(df.loc[df[df.full_date > test_date].index, 'full_date'], df.loc[df[df.full_date > test_date].index, 'real'])
ax.axvline(x=test_date, color='k', linestyle='--', lw=2)
ax.axvline(x=test_date - dt.timedelta(days=6*30), color='k', linestyle='--', lw=2)
ax.grid()
ax.set_title("Wind Power", fontsize=16)
ax.set_xlabel("Date", fontsize=12)
ax.set_ylabel("GWh", fontsize=12)
ax.text(test_date - dt.timedelta(days=15), 19, "Quantile Regression predictions", ha='right', va='top', rotation=0, fontsize=8)
ax.annotate("", xy=(test_date - dt.timedelta(days=5), 18.25), xytext=(test_date - dt.timedelta(days=6*30), 18.25),
arrowprops=dict(arrowstyle="->"))
ax.text(test_date + dt.timedelta(days=6*30) - dt.timedelta(days=15), 19, "Conformal prediction methods", ha='right', va='top', rotation=0, fontsize=8)
ax.annotate("", xy=(test_date + dt.timedelta(days=6*30), 18.25), xytext=(test_date, 18.25),
arrowprops=dict(arrowstyle="->"))
plt.tight_layout()
plt.savefig(d + "\\Images\\wind_power_figure.pdf")
# plt.show()
def generate_coefs_variation():
d = dirname(abspath(__file__))
with open('dict_coefs_cfqra_alpha_0.01.pkl', 'rb') as file:
dict_coefs_cfqra_001 = pickle.load(file)
with open('dict_coefs_cfqra_alpha_0.05.pkl', 'rb') as file:
dict_coefs_cfqra_005 = pickle.load(file)
with open('dict_coefs_cfqra_alpha_0.1.pkl', 'rb') as file:
dict_coefs_cfqra_01 = pickle.load(file)
with open('dict_coefs_cfqra_alpha_0.2.pkl', 'rb') as file:
dict_coefs_cfqra_02 = pickle.load(file)
fig, ax = plt.subplots(1, 1, figsize=(10, 4))
# ax.plot(dict_coefs_cfqra_001['inf'], label='$\\alpha=0.01$', color=colors[0])
# ax.plot(dict_coefs_cfqra_001['sup'], '--', color=colors[0])
ax.plot(dict_coefs_cfqra_005['inf'], label='$\\alpha=0.05$', color=colors[0])
ax.plot(dict_coefs_cfqra_005['sup'], '--', color=colors[0])
ax.plot(dict_coefs_cfqra_01['inf'], label='$\\alpha=0.10$', color=colors[1])
ax.plot(dict_coefs_cfqra_01['sup'], '--', color=colors[1])
ax.plot(dict_coefs_cfqra_02['inf'], label='$\\alpha=0.20$', color=colors[2])
ax.plot(dict_coefs_cfqra_02['sup'], '--', color=colors[2])
ax.set_title("$\\hat{\\lambda}_{2}(\\frac{\\alpha}{2})$ and $\\hat{\\lambda}_{2}(1-\\frac{\\alpha}{2})$ over time", fontsize=16)
ax.set_xlabel("T+1", fontsize=12)
ax.set_ylabel("Coefficient value", fontsize=12)
ax.legend()
plt.tight_layout()
ax.grid()
# plt.show()
plt.savefig(d+"\\Images\\coefs_variation.pdf")
def generate_coefs_models_qra():
d = dirname(abspath(__file__))
with open('dict_coefs_qra_alpha_0.01.pkl', 'rb') as file:
dict_coefs_cfqra_001 = pickle.load(file)
with open('dict_coefs_qra_alpha_0.05.pkl', 'rb') as file:
dict_coefs_cfqra_005 = pickle.load(file)
with open('dict_coefs_qra_alpha_0.1.pkl', 'rb') as file:
dict_coefs_cfqra_01 = pickle.load(file)
with open('dict_coefs_qra_alpha_0.2.pkl', 'rb') as file:
dict_coefs_cfqra_02 = pickle.load(file)
fig, ax = plt.subplots(4, 1, figsize=(10, 16))
ax[0].plot(dict_coefs_cfqra_001['inf']['pred1'], label='Model 1', color=colors[0])
# ax[0].plot(dict_coefs_cfqra_001['sup']['pred1'], '--', color=colors[0])
ax[0].plot(dict_coefs_cfqra_001['inf']['pred2'], label='Model 2', color=colors[1])
# ax[0].plot(dict_coefs_cfqra_001['sup']['pred2'], '--', color=colors[1])
ax[0].plot(dict_coefs_cfqra_001['inf']['pred3'], label='Model 3', color=colors[2])
# ax[0].plot(dict_coefs_cfqra_001['sup']['pred3'], '--', color=colors[2])
ax[0].set_title("$\\alpha = 0.01$", fontsize=16)
ax[0].set_xlabel("T+1", fontsize=12)
ax[0].set_ylabel("Coefficient value", fontsize=12)
ax[0].legend()
ax[0].grid()
ax[1].plot(dict_coefs_cfqra_005['inf']['pred1'], label='Model 1', color=colors[0])
# ax[1].plot(dict_coefs_cfqra_005['sup']['pred1'], '--', color=colors[0])
ax[1].plot(dict_coefs_cfqra_005['inf']['pred2'], label='Model 2', color=colors[1])
# ax[1].plot(dict_coefs_cfqra_005['sup']['pred2'], '--', color=colors[1])
ax[1].plot(dict_coefs_cfqra_005['inf']['pred3'], label='Model 3', color=colors[2])
# ax[1].plot(dict_coefs_cfqra_005['sup']['pred3'], '--', color=colors[2])
ax[1].set_title("$\\alpha = 0.05$", fontsize=16)
ax[1].set_xlabel("T+1", fontsize=12)
ax[1].set_ylabel("Coefficient value", fontsize=12)
ax[1].legend()
ax[1].grid()
ax[2].plot(dict_coefs_cfqra_01['inf']['pred1'], label='Model 1', color=colors[0])
# ax[2].plot(dict_coefs_cfqra_01['sup']['pred1'], '--', color=colors[0])
ax[2].plot(dict_coefs_cfqra_01['inf']['pred2'], label='Model 2', color=colors[1])
# ax[2].plot(dict_coefs_cfqra_01['sup']['pred2'], '--', color=colors[1])
ax[2].plot(dict_coefs_cfqra_01['inf']['pred3'], label='Model 3', color=colors[2])
# ax[2].plot(dict_coefs_cfqra_01['sup']['pred3'], '--', color=colors[2])
ax[2].set_title("$\\alpha = 0.10$", fontsize=16)
ax[2].set_xlabel("T+1", fontsize=12)
ax[2].set_ylabel("Coefficient value", fontsize=12)
ax[2].legend()
ax[2].grid()
ax[3].plot(dict_coefs_cfqra_02['inf']['pred1'], label='Model 1', color=colors[0])
# ax[3].plot(dict_coefs_cfqra_02['sup']['pred1'], '--', color=colors[0])
ax[3].plot(dict_coefs_cfqra_02['inf']['pred2'], label='Model 2', color=colors[1])
# ax[3].plot(dict_coefs_cfqra_02['sup']['pred2'], '--', color=colors[1])
ax[3].plot(dict_coefs_cfqra_02['inf']['pred3'], label='Model 3', color=colors[2])
# ax[3].plot(dict_coefs_cfqra_02['sup']['pred3'], '--', color=colors[3])
ax[3].set_title("$\\alpha = 0.20$", fontsize=16)
ax[3].set_xlabel("T+1", fontsize=12)
ax[3].set_ylabel("Coefficient value", fontsize=12)
ax[3].legend()
ax[3].grid()
plt.tight_layout()
# plt.show()
plt.savefig(d+"\\Images\\coefs_variation_qra.pdf")
def generate_coefs_variation_wind():
d = dirname(abspath(__file__))
# with open('dict_coefs_cfqra_wind_alpha_0.01.pkl', 'rb') as file:
# dict_coefs_cfqra_001 = pickle.load(file)
with open('dict_coefs_cfqra_wind_alpha_0.05.pkl', 'rb') as file:
dict_coefs_cfqra_005 = pickle.load(file)
with open('dict_coefs_cfqra_wind_alpha_0.1.pkl', 'rb') as file:
dict_coefs_cfqra_01 = pickle.load(file)
with open('dict_coefs_cfqra_wind_alpha_0.2.pkl', 'rb') as file:
dict_coefs_cfqra_02 = pickle.load(file)
fig, ax = plt.subplots(1, 1, figsize=(10, 4))
# ax.plot(dict_coefs_cfqra_001['inf'], label='$\\alpha=0.01$', color=colors[0])
# ax.plot(dict_coefs_cfqra_001['sup'], '--', color=colors[0])
ax.plot(dict_coefs_cfqra_005['inf'], label='$\\alpha=0.05$', color=colors[0])
ax.plot(dict_coefs_cfqra_005['sup'], '--', color=colors[0])
ax.plot(dict_coefs_cfqra_01['inf'], label='$\\alpha=0.10$', color=colors[1])
ax.plot(dict_coefs_cfqra_01['sup'], '--', color=colors[1])
ax.plot(dict_coefs_cfqra_02['inf'], label='$\\alpha=0.20$', color=colors[2])
ax.plot(dict_coefs_cfqra_02['sup'], '--', color=colors[2])
ax.set_title("$\\hat{\\lambda}_{2}(\\frac{\\alpha}{2})$ and $\\hat{\\lambda}_{2}(1-\\frac{\\alpha}{2})$ over time", fontsize=16)
ax.set_xlabel("T+1", fontsize=12)
ax.set_ylabel("Coefficient value", fontsize=12)
ax.legend()
plt.tight_layout()
ax.grid()
# plt.show()
plt.savefig(d+"\\Images\\coefs_variation_wind.pdf")
def generate_progression_alphas_2(hour=13):
d = dirname(abspath(__file__))
with open('dict_list_alphas_aci.pkl', 'rb') as file:
list_alphas_aci = pickle.load(file)
with open('dict_list_alphas_waci.pkl', 'rb') as file:
list_alphas_waci = pickle.load(file)
with open('dict_list_alphas_waci_2.pkl', 'rb') as file:
list_alphas_waci_2 = pickle.load(file)
list_alphas_aci = list_alphas_aci[hour]
list_alphas_waci = list_alphas_waci[hour]
list_alphas_waci_2 = list_alphas_waci_2[hour]
for i in range(len(list_alphas_aci)):
fig, ax = plt.subplots(1, 1, figsize=(10, 4))
ax.axhline(0.2, xmin= 0, xmax=500, lw=1, color='k', linestyle='--', label='Objective miscoverage', alpha=0.4)
ax.plot(np.arange(0, 500, 0.1), list_alphas_waci[i], color=colors[0], label='WACI', lw=1)
ax.plot(np.arange(0, 500, 0.1), list_alphas_waci_2[i], color=colors[1], label='WACI 2', lw=1)
ax.axhline(list_alphas_aci[i], xmin= 0, xmax=500, lw=1, color=colors[2], label='ACI')
ax.set_xlabel('Interval length', fontsize=14)
ax.set_ylabel("$\\alpha_t$", fontsize=14)
ax.grid()
ax.set_ylim(0.1, 0.3)
ax.set_xlim(0, 100)
ax.legend(prop={'size': 14})
ax.tick_params(axis='x', labelsize=12)
ax.tick_params(axis='y', labelsize=12)
plt.tight_layout()
plt.savefig(d + f"\\Images\\alpha_progression_2\\alpha_progression_hour{hour}_{i}.pdf")
# generate_expected_error_3d_plot()
# generate_day_ahead_market_figure()
# generate_day_ahead_market_predictions()
# generate_progression_alphas(hour=13)
# generate_wind_forecasts()
# generate_wind_power_figure()
# generate_coefs_variation()
# generate_coefs_models_qra()
# generate_coefs_variation_wind()
# generate_progression_alphas_2(hour=13)