Plot Spectrum Noise

In [30]:

import os
import pandas as pd
import matplotlib.pyplot as plt
import scienceplots
import matplotlib as mpl
import matplotlib
# Default settings
mpl.rcParams.update(mpl.rcParamsDefault)
# plt.style.use('science')
plt.style.use("seaborn-darkgrid")
plt.rcParams.update({'font.size': 14})

import os import pandas as pd import matplotlib.pyplot as plt import scienceplots import matplotlib as mpl import matplotlib # Default settings mpl.rcParams.update(mpl.rcParamsDefault) # plt.style.use('science') plt.style.use("seaborn-darkgrid") plt.rcParams.update({'font.size': 14})

C:\Users\dicky1031\AppData\Local\Temp\ipykernel_14136\779055010.py:10: MatplotlibDeprecationWarning: The seaborn styles shipped by Matplotlib are deprecated since 3.6, as they no longer correspond to the styles shipped by seaborn. However, they will remain available as 'seaborn-v0_8-<style>'. Alternatively, directly use the seaborn API instead.
  plt.style.use("seaborn-darkgrid")

In [31]:

result_folder = 'prediction_model_formula8'

result_folder = 'prediction_model_formula8'

In [32]:

fix_noise_data = pd.read_csv(os.path.join('pic', result_folder, 'all_train_data_on_test_set','fix_noise_result.csv'))
fix_noise = fix_noise_data['noise']
fix_noise_RMSE = fix_noise_data['RMSE']

rand_noise_data = pd.read_csv(os.path.join('pic', result_folder, 'all_train_data_on_test_set','random_noise_result.csv'))
rand_noise = rand_noise_data['noise']
rand_noise_RMSE = rand_noise_data['RMSE']

fix_noise_data = pd.read_csv(os.path.join('pic', result_folder, 'all_train_data_on_test_set','fix_noise_result.csv')) fix_noise = fix_noise_data['noise'] fix_noise_RMSE = fix_noise_data['RMSE'] rand_noise_data = pd.read_csv(os.path.join('pic', result_folder, 'all_train_data_on_test_set','random_noise_result.csv')) rand_noise = rand_noise_data['noise'] rand_noise_RMSE = rand_noise_data['RMSE']

In [37]:

plt.figure(figsize=(8,6))
plt.plot(fix_noise*100, fix_noise_RMSE, 'o-')
plt.title('spectrum with fixed noise')
plt.xlabel('fixed noise(%)')
plt.ylabel('prediction performance, RMSE(%)')
plt.savefig(os.path.join("pic", result_folder, "fixed_noise_result.png"), dpi=300, format='png', bbox_inches='tight')
plt.show()

plt.figure(figsize=(8,6)) plt.plot(fix_noise*100, fix_noise_RMSE, 'o-') plt.title('spectrum with fixed noise') plt.xlabel('fixed noise(%)') plt.ylabel('prediction performance, RMSE(%)') plt.savefig(os.path.join("pic", result_folder, "fixed_noise_result.png"), dpi=300, format='png', bbox_inches='tight') plt.show()

In [38]:

plt.figure(figsize=(8,6))
plt.plot(rand_noise*100, rand_noise_RMSE, 'o-')
plt.title('spectrum with random noise')
plt.xlabel('random noise(%)')
plt.ylabel('prediction performance, RMSE(%)')
plt.savefig(os.path.join("pic", result_folder, "random_noise_result.png"), dpi=300, format='png', bbox_inches='tight')
plt.show()

plt.figure(figsize=(8,6)) plt.plot(rand_noise*100, rand_noise_RMSE, 'o-') plt.title('spectrum with random noise') plt.xlabel('random noise(%)') plt.ylabel('prediction performance, RMSE(%)') plt.savefig(os.path.join("pic", result_folder, "random_noise_result.png"), dpi=300, format='png', bbox_inches='tight') plt.show()