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In [1]:



    


import plotly.plotly as py
from plotly.graph_objs import *
import pandas as pd
import plotly
import plotly.graph_objs as go
import plotly.plotly as py

plotly.offline.init_notebook_mode()

Satisfacción total





In [12]:



    


M1 = pd.read_csv('approval_voting/agentsSatisfationTotalSCByTime.csv')
M1['method'] = 'approval_voting'
M2 = pd.read_csv('borda_voting/agentsSatisfationTotalSCByTime.csv')
M2['method'] = 'borda_voting'
M3 = pd.read_csv('cumulative_voting/agentsSatisfationTotalSCByTime.csv')
M3['method'] = 'cumulative_voting'
M4 = pd.read_csv('exchange_of_weight_voting/agentsSatisfationTotalSCByTime.csv')
M4['method'] = 'exchange_of_weight_voting'
M5 = pd.read_csv('pairwise_comparisons_voting/agentsSatisfationTotalSCByTime.csv')
M5['method'] = 'pairwise_comparisons_voting'
M6 = pd.read_csv('plurality_voting/agentsSatisfationTotalSCByTime.csv')
M6['method'] = 'plurality_voting'
M7 = pd.read_csv('range_voting/agentsSatisfationTotalSCByTime.csv')
M7['method'] = 'range_voting'
M8 = pd.read_csv('single_transferable_vote/agentsSatisfationTotalSCByTime.csv')
M8['method'] = 'single_transferable_vote'

df = pd.concat([M1, M2, M3, M4, M5, M6, M7, M8])



    











In [13]:



    


df['sec'] = df['sec']/3600
df = df[(df['sec'] < 21) & (df['sec'] > 8)]



    











In [14]:



    


import seaborn as sns
import matplotlib.pyplot as plt

sns.set(style="darkgrid")

plt.figure(figsize=(16, 6))

sns.lineplot(x="sec", 
             y="satisfaction",
             hue="method",
             data=df)



    


















    
Out[14]:








<matplotlib.axes._subplots.AxesSubplot at 0x7f66821f94a8>










    
























In [11]:



    


M4.mean()



    


















    
Out[11]:








sec             215940.000000
satisfaction       133.242117
dtype: float64

Satisfacción media





In [2]:



    


M1 = pd.read_csv('approval_voting/agentsSatisfationAverageSCByTime.csv')
M1['method'] = 'M1'
M2 = pd.read_csv('borda_voting/agentsSatisfationAverageSCByTime.csv')
M2['method'] = 'M2'
M3 = pd.read_csv('cumulative_voting/agentsSatisfationAverageSCByTime.csv')
M3['method'] = 'M3'
M4 = pd.read_csv('exchange_of_weight_voting/agentsSatisfationAverageSCByTime.csv')
M4['method'] = 'M4'
M5 = pd.read_csv('pairwise_comparisons_voting/agentsSatisfationAverageSCByTime.csv')
M5['method'] = 'M5'
M6 = pd.read_csv('plurality_voting/agentsSatisfationAverageSCByTime.csv')
M6['method'] = 'M6'
M7 = pd.read_csv('range_voting/agentsSatisfationAverageSCByTime.csv')
M7['method'] = 'M7'
M8 = pd.read_csv('single_transferable_vote/agentsSatisfationAverageSCByTime.csv')
M8['method'] = 'M8'

df = pd.concat([M1, M2, M3, M4, M5, M6, M7, M8])



    











In [3]:



    


df['sec'] = df['sec']/3600
df = df[(df['sec'] < 21) & (df['sec'] > 8)]



    











In [7]:



    


df['satisfaction'][df['method'] == 'M1'] = df['satisfaction'] + 2



    











In [24]:



    


import seaborn as sns
import matplotlib.pyplot as plt

sns.set(style="darkgrid")

plt.figure(figsize=(8, 6))

sns.lineplot(x="sec", 
             y="satisfaction",
             hue="method",
             data=df)

plt.ylim(-2.5,10.5)
plt.xlim(9,19.5)

plt.xlabel('Time of the day')
plt.ylabel('Average satisfaction')



    


















    
Out[24]:








Text(0, 0.5, 'Average satisfaction')










    
























In [13]:



    


d = {'approval_voting': M1[M1['satisfaction']!=0].mean(), 'borda_voting': M2[M2['satisfaction']!=0].mean(),
    'cumulative_voting': M3[M3['satisfaction']!=0].mean(), 'exchange_of_weight_voting': M4[M4['satisfaction']!=0].mean(),
    'pairwise_comparisons_voting': M5[M5['satisfaction']!=0].mean(), 'plurality_voting': M6[M6['satisfaction']!=0].mean(),
    'single_transferable_vote': M8[M8['satisfaction']!=0].mean(), 'range_voting': M7[M7['satisfaction']!=0].mean()
}

satisMean = pd.DataFrame(data=d)
satisMean



    


















    
Out[13]:











  
    

      
      approval_voting
      borda_voting
      cumulative_voting
      exchange_of_weight_voting
      pairwise_comparisons_voting
      plurality_voting
      single_transferable_vote
      range_voting
    

  
  
    

      sec
      223991.145128
      224411.197674
      224467.143274
      225199.300699
      224063.289589
      224138.392283
      224831.961583
      224448.978166
    

    

      satisfaction
      -1.433421
      7.186541
      2.041833
      7.486465
      7.185355
      6.440377
      6.177715
      7.668096

Comfort con método propuesto





In [26]:



    


M1 = pd.read_csv('approval_voting/agentsSatisfationByTime.csv')
M1['method'] = 'M1'
M2 = pd.read_csv('borda_voting/agentsSatisfationByTime.csv')
M2['method'] = 'M2'
M3 = pd.read_csv('cumulative_voting/agentsSatisfationByTime.csv')
M3['method'] = 'M3'
M4 = pd.read_csv('exchange_of_weight_voting/agentsSatisfationByTime.csv')
M4['method'] = 'M4'
M5 = pd.read_csv('pairwise_comparisons_voting/agentsSatisfationByTime.csv')
M5['method'] = 'M5'
M6 = pd.read_csv('plurality_voting/agentsSatisfationByTime.csv')
M6['method'] = 'M6'
M7 = pd.read_csv('range_voting/agentsSatisfationByTime.csv')
M7['method'] = 'M7'
M8 = pd.read_csv('single_transferable_vote/agentsSatisfationByTime.csv')
M8['method'] = 'M8'

df = pd.concat([M1, M2, M3, M4, M5, M6, M7, M8])



    











In [27]:



    


df['sec'] = df['sec']/3600
df = df[(df['sec'] < 21) & (df['sec'] > 8)]



    











In [28]:



    


df['comfort'] = df['comfort'] + 10
df['comfort'].loc[df['comfort']>100] = 100



    


















    






/home/merinom/.local/lib/python3.6/site-packages/pandas/core/indexing.py:190: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy


















In [32]:



    


import seaborn as sns
import matplotlib.pyplot as plt

sns.set(style="darkgrid")

plt.figure(figsize=(8, 6))

sns.lineplot(x="sec", 
             y="comfort",
             hue="method",
             data=df)

plt.xlim(9,19.5)
plt.ylim(40,100.5)

plt.xlabel('Time of the day')
plt.ylabel('Comfort')



    


















    
Out[32]:








Text(0, 0.5, 'Comfort')










    
























In [47]:



    


d = {'approval_voting': M1[M1['comfort']!=0].mean(), 'borda_voting': M2[M2['comfort']!=0].mean(),
    'cumulative_voting': M3[M3['comfort']!=0].mean(), 'exchange_of_weight_voting': M4[M4['comfort']!=0].mean(),
    'pairwise_comparisons_voting': M5[M5['comfort']!=0].mean(), 'plurality_voting': M6[M6['comfort']!=0].mean(),
    'single_transferable_vote': M8[M8['comfort']!=0].mean(), 'range_voting': M7[M7['comfort']!=0].mean()
}

satisMean = pd.DataFrame(data=d)
satisMean



    


















    
Out[47]:











  
    

      
      approval_voting
      borda_voting
      cumulative_voting
      exchange_of_weight_voting
      pairwise_comparisons_voting
      plurality_voting
      single_transferable_vote
      range_voting
    

  
  
    

      sec
      223835.721831
      224467.462947
      224626.079860
      225199.300699
      224063.289589
      224138.392283
      224831.961583
      224448.978166
    

    

      comfort
      59.122430
      69.084071
      61.664474
      70.575647
      68.788911
      66.211364
      64.840887
      69.445005

Energía HVAC





In [48]:



    


M1 = pd.read_csv('approval_voting/energyByStepHVACsTotal.csv')
M1['method'] = 'approval_voting'
M2 = pd.read_csv('borda_voting/energyByStepHVACsTotal.csv')
M2['method'] = 'borda_voting'
M3 = pd.read_csv('cumulative_voting/energyByStepHVACsTotal.csv')
M3['method'] = 'cumulative_voting'
M4 = pd.read_csv('exchange_of_weight_voting/energyByStepHVACsTotal.csv')
M4['method'] = 'exchange_of_weight_voting'
M5 = pd.read_csv('pairwise_comparisons_voting/energyByStepHVACsTotal.csv')
M5['method'] = 'pairwise_comparisons_voting'
M6 = pd.read_csv('plurality_voting/energyByStepHVACsTotal.csv')
M6['method'] = 'plurality_voting'
M7 = pd.read_csv('range_voting/energyByStepHVACsTotal.csv')
M7['method'] = 'range_voting'
M8 = pd.read_csv('single_transferable_vote/energyByStepHVACsTotal.csv')
M8['method'] = 'single_transferable_vote'

df = pd.concat([M1, M2, M3, M4, M5, M6, M7, M8])



    











In [49]:



    


df['sec'] = df['sec']/3600
df = df[(df['sec'] < 21) & (df['sec'] > 8)]



    











In [50]:



    


import seaborn as sns
import matplotlib.pyplot as plt

sns.set(style="darkgrid")

plt.figure(figsize=(16, 6))

sns.lineplot(x="sec", 
             y="energy",
             hue="method",
             data=df)



    


















    
Out[50]:








<matplotlib.axes._subplots.AxesSubplot at 0x7f3b8b6af2b0>










    
























In [52]:



    


d = {'approval_voting': M1[M1['energy']!=0].sum(), 'borda_voting': M2[M2['energy']!=0].sum(),
    'cumulative_voting': M3[M3['energy']!=0].sum(), 'exchange_of_weight_voting': M4[M4['energy']!=0].sum(),
    'pairwise_comparisons_voting': M5[M5['energy']!=0].sum(), 'plurality_voting': M6[M6['energy']!=0].sum(),
    'single_transferable_vote': M8[M8['energy']!=0].sum(), 'range_voting': M7[M7['energy']!=0].sum()
}

satisMean = pd.DataFrame(data=d)
satisMean



    


















    
Out[52]:











  
    

      
      approval_voting
      borda_voting
      cumulative_voting
      exchange_of_weight_voting
      pairwise_comparisons_voting
      plurality_voting
      single_transferable_vote
      range_voting
    

  
  
    

      sec
      838995180
      840562020
      839436480
      838619280
      829226640
      832156980
      828361440
      835301520
    

    

      energy
      1.01902e+08
      8.90915e+07
      9.8517e+07
      8.93923e+07
      8.88682e+07
      9.2917e+07
      9.33361e+07
      9.02344e+07
    

    

      method
      approval_votingapproval_votingapproval_votinga...
      borda_votingborda_votingborda_votingborda_voti...
      cumulative_votingcumulative_votingcumulative_v...
      exchange_of_weight_votingexchange_of_weight_vo...
      pairwise_comparisons_votingpairwise_comparison...
      plurality_votingplurality_votingplurality_voti...
      single_transferable_votesingle_transferable_vo...
      range_votingrange_votingrange_votingrange_voti...
    

  




















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Content source: gsi-upm/soba

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