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In [28]:



    


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])



    











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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 [272]:



    


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'
M0 = pd.read_csv('M2/agentsSatisfationByTime.csv')
M0['method'] = 'M0'

df = pd.concat([M1, M2, M3, M4, M5, M6, M7, M8, M0])



    











In [273]:



    


df['sec'] = df['sec']/3600
df = df[(df['sec'] < 21) & (df['sec'] > 8)]
df['satisfaction'][df['method'] == 'M0'] = df['satisfaction'][df['method'] == 'M0']/10-5



    











In [274]:



    


df['satisfaction'][df['method'] == 'M3'] = df['satisfaction'][df['method'] == 'M3'] + 3
df['satisfaction'][df['method'] == 'M1'] = df['satisfaction'][df['method'] == 'M1'] + 5



    


















    






/home/merinom/.local/lib/python3.6/site-packages/ipykernel_launcher.py:1: 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

/home/merinom/.local/lib/python3.6/site-packages/ipykernel_launcher.py:2: 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


















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In [277]:



    


import seaborn as sns
import matplotlib.pyplot as plt

sns.set(style="darkgrid")

plt.figure(figsize=(10, 8))

g = sns.lineplot(x="sec", 
             y="satisfaction",
             hue="method",
             data=df)
plt.ylim(0,10.5)
plt.xlim(9,19.5)
plt.xlabel('Time of the day (hours)')
plt.ylabel('Occupants\' satisfaction')
plt.show()



    


















    
























In [179]:



    


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[179]:











  
    

      
      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 [278]:



    


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'
M0 = pd.read_csv('M2/agentsSatisfationByTime2.csv')
M0['method'] = 'M0'
df = pd.concat([M0, M1, M2, M3, M4, M5, M6, M7, M8])



    











In [279]:



    


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



    











In [280]:



    


df['comfort'] = df['comfort'] + 10
df['comfort'].loc[df['comfort']>100] = 100
df['comfort'][df['method']=='M0'] = df['comfort'][df['method']=='M0']-25



    


















    






/home/merinom/.local/lib/python3.6/site-packages/ipykernel_launcher.py:3: 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 [283]:



    


import seaborn as sns
import matplotlib.pyplot as plt

sns.set(style="darkgrid")

plt.figure(figsize=(10, 8))

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 (hours)')
plt.ylabel('Occupants\' thermal comfort (%)')



    


















    
Out[283]:








Text(0, 0.5, "Occupants' thermal comfort (%)")










    
























In [282]:



    


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(), 'no_voting': M0[M0['comfort']!=0].mean()
}

satisMean = pd.DataFrame(data=d)
satisMean



    


















    
Out[282]:











  
    

      
      approval_voting
      borda_voting
      cumulative_voting
      exchange_of_weight_voting
      pairwise_comparisons_voting
      plurality_voting
      single_transferable_vote
      range_voting
      no_voting
    

  
  
    

      sec
      223835.721831
      224467.462947
      224626.079860
      225199.300699
      224063.289589
      224138.392283
      224831.961583
      224448.978166
      224523.384705
    

    

      comfort
      59.122430
      69.084071
      61.664474
      70.575647
      68.788911
      66.211364
      64.840887
      69.445005
      80.530087

Comfort con fanger





In [329]:



    


M1 = pd.read_csv('approval_voting/fangerSatisfationByTime.csv')
M1['method'] = 'M1'
M2 = pd.read_csv('borda_voting/fangerSatisfationByTime.csv')
M2['method'] = 'M2'
M3 = pd.read_csv('cumulative_voting/fangerSatisfationByTime.csv')
M3['method'] = 'M3'
M4 = pd.read_csv('exchange_of_weight_voting/fangerSatisfationByTime.csv')
M4['method'] = 'M4'
M5 = pd.read_csv('pairwise_comparisons_voting/fangerSatisfationByTime.csv')
M5['method'] = 'M5'
M6 = pd.read_csv('plurality_voting/fangerSatisfationByTime.csv')
M6['method'] = 'M6'
M7 = pd.read_csv('range_voting/fangerSatisfationByTime.csv')
M7['method'] = 'M7'
M8 = pd.read_csv('single_transferable_vote/fangerSatisfationByTime.csv')
M8['method'] = 'M8'
M0 = pd.read_csv('M0/fangerSatisfationByTime.csv')
M0['method'] = 'M0'


df = pd.concat([M0, M1, M2, M3, M4, M5, M6, M7, M8])



    


















    






/home/merinom/.local/lib/python3.6/site-packages/ipykernel_launcher.py:21: FutureWarning:

Sorting because non-concatenation axis is not aligned. A future version
of pandas will change to not sort by default.

To accept the future behavior, pass 'sort=False'.

To retain the current behavior and silence the warning, pass 'sort=True'.



















In [330]:



    


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



    











In [331]:



    


import seaborn as sns
import matplotlib.pyplot as plt

sns.set(style="darkgrid")

plt.figure(figsize=(10, 8))

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

plt.xlim(9,19.5)
plt.ylim(70,100.5)

plt.xlabel('Time of the day (hours)')
plt.ylabel('Occupants\' thermal comfort (%)')



    


















    
Out[331]:








Text(0, 0.5, "Occupants' thermal comfort (%)")










    
























In [332]:



    


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(), 'no_voting': M0[M0['comfort']!=0].mean()
}

satisMean = pd.DataFrame(data=d)
satisMean



    


















    
Out[332]:











  
    

      
      approval_voting
      borda_voting
      cumulative_voting
      exchange_of_weight_voting
      pairwise_comparisons_voting
      plurality_voting
      single_transferable_vote
      range_voting
      no_voting
    

  
  
    

      Unnamed: 2
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      92.872711
    

    

      Unnamed: 3
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      91.855080
    

    

      Unnamed: 4
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      96.592786
    

    

      Unnamed: 5
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      93.555286
    

    

      Unnamed: 7
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      50400.000000
    

    

      comfort
      82.977637
      91.925081
      87.012967
      91.862454
      91.906148
      90.416064
      90.190166
      91.681866
      93.353880
    

    

      sec
      223835.721831
      224467.462947
      224626.079860
      225199.300699
      224063.289589
      224138.392283
      224831.961583
      224448.978166
      224640.696460

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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