Rohit Garg
208 linhas
4.5 KiB
Python
208 linhas
4.5 KiB
Python
#!/usr/bin/env python
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# coding: utf-8
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import glob
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import pandas as pd
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import matplotlib.pyplot as plt
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from google.colab import drive
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drive.mount('/gdrive',force_remount=True)
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get_ipython().run_line_magic('cd', '/gdrive/MyDrive/emotion_recognition_project/')
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# Script to obtain the incremental learning graph for the DEAP, DREAMER and OASIS datasets.
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# ##plots for DEAP
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dataset_deap=glob.glob('plots/deap/*.csv')
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dataset_deap
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dataset_svr_deap = pd.read_csv(dataset_deap[0]).T
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dataset_svr_deap.columns = ['valence','arousal']
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dataset_svr_deap = dataset_svr_deap.drop('Unnamed: 0')
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dataset_svr_deap= dataset_svr_deap[::1]
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x_deap = range(1,33,1)
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dataset_svr_deap
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fig_deap,axe_deap = plt.subplots(1,1,figsize = (17,10))
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axe_deap.plot(x_deap,dataset_svr_deap['valence'],color='green',marker = 'x',markersize=10)
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axe_deap.plot(x_deap,dataset_svr_deap['arousal'],color ='red',marker = 'x',markersize=10)
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axe_deap.legend(['rfr_valence','rfr_arousal'],)
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axe_deap.set_xlabel('trained upto subject')
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axe_deap.set_ylabel('RMSE values')
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plt.rcParams.update({'font.size':40})
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plt.tight_layout()
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plt.xticks(x_deap[::3])
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fig_deap.savefig('final_plots/deap_rfr__valence_arousal_rms.svg')
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fig_deap.savefig('final_plots/deap_rfr__valence_arousal_rms.png')
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# ##plots for DREAMER
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dataset_dreamer=glob.glob('plots/dreamer/*.csv')
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dataset_dreamer
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dataset_svr_dreamer = pd.read_csv(dataset_dreamer[0]).T
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dataset_svr_dreamer.columns = ['valence','arousal']
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dataset_svr_dreamer = dataset_svr_dreamer.drop('Unnamed: 0')
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x_dreamer = range(1,24,1)
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dataset_svr_dreamer= dataset_svr_dreamer[::1]
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dataset_svr_dreamer
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fig_dreamer,axe_dreamer = plt.subplots(1,1,figsize=(17,10))
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axe_dreamer.plot(x_dreamer,dataset_svr_dreamer['valence'],color='green',marker = 'x',markersize=10)
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axe_dreamer.plot(x_dreamer,dataset_svr_dreamer['arousal'],color ='red',marker = 'x',markersize=10)
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axe_dreamer.legend(['rfr_valence','rfr_arousal'],)
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axe_dreamer.set_xlabel('trained upto subject')
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axe_dreamer.set_ylabel('RMSE values')
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plt.rcParams.update({'font.size':40})
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plt.tight_layout()
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plt.xticks(x_dreamer[::3])
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fig_dreamer.savefig('final_plots/dreamer_rfr__valence_arousal_rms.svg')
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fig_dreamer.savefig('final_plots/dreamer_rfr__valence_arousal_rms.png')
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# ##plots for oasis
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dataset_oasis=glob.glob('plots/oasis/*.csv')
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dataset_oasis
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dataset_svr_oasis = pd.read_csv(dataset_oasis[0]).T
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dataset_svr_oasis.columns = ['valence','arousal']
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dataset_svr_oasis = dataset_svr_oasis.drop('Unnamed: 0')
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x_oasis = range(1,16,1)
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dataset_svr_oasis= dataset_svr_oasis[::1]
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dataset_svr_oasis
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fig_oasis,axe_oasis = plt.subplots(1,1,figsize=(17,10))
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axe_oasis.plot(x_oasis,dataset_svr_oasis['valence'],color='green',marker = 'x',markersize=10)
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axe_oasis.plot(x_oasis,dataset_svr_oasis['arousal'],color ='red',marker = 'x',markersize=10)
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axe_oasis.set_xlabel('trained upto subject')
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axe_oasis.set_ylabel('RMSE values')
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axe_oasis.legend(['rfr_valence','rfr_arousal'],loc = 'lower right')
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plt.rcParams.update({'font.size':40})
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plt.xticks(x_oasis[::3])
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plt.tight_layout()
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fig_oasis.savefig('final_plots/oasis_rfr__valence_arousal_rms.svg')
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fig_oasis.savefig('final_plots/oasis_rfr__valence_arousal_rms.png')
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f,a = plt.subplots(3,1,figsize = (40,30))
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a[0].plot(x_deap,dataset_svr_deap['valence'],color='green',marker = 'x',markersize=10)
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a[0].plot(x_deap,dataset_svr_deap['arousal'],color ='red',marker = 'x',markersize=10)
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a[0].legend(['svr_valence','svr_arousal','rfr_valence','rfr_arousal'],)
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#a[0].set_xlabel('trained upto subject')
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a[0].set_ylabel('RMSE values')
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a[0].set_title('DEAP')
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a[1].plot(x_dreamer,dataset_svr_dreamer['valence'],color='green',marker = 'x',markersize=10)
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a[1].plot(x_dreamer,dataset_svr_dreamer['arousal'],color ='red',marker = 'x',markersize=10)
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#a[1].legend(['svr_valence','svr_arousal','rfr_valence','rfr_arousal'],)
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#a[1].set_xlabel('trained upto subject')
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a[1].set_ylabel('RMSE values')
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a[1].set_title('DREAMER')
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a[2].plot(x_oasis,dataset_svr_oasis['valence'],color='green',marker = 'x',markersize=10)
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a[2].plot(x_oasis,dataset_svr_oasis['arousal'],color ='red',marker = 'x',markersize=10)
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a[2].set_xlabel('trained upto subject')
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a[2].set_ylabel('RMSE values')
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#a[2].legend(['svr_valence','svr_arousal','rfr_valence','rfr_arousal'],loc = 'lower right')
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a[2].set_title('OASIS')
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plt.rcParams.update({'font.size':40})
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plt.tight_layout()
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f.savefig('final_plots/all_plots_incremental learning.svg')
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