In [35]:

    

import json
import pandas as pd
from datetime import datetime
import numpy as np
import math
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
import plotly.graph_objs as go
from plotly import tools
import plotly.plotly as py
import cufflinks as cf
cf.go_online()

inpExifFl = "../data/GZC_exifs_beauty_full.json"

with open(inpExifFl,"r") as inpJsonFl:
    exifJsonObj = json.load(inpJsonFl)


    

In [36]:

    

exifDf = pd.DataFrame(exifJsonObj).transpose()

exifDf['datetime'] = exifDf['datetime'].apply(lambda x : datetime.strptime(x,'%Y-%m-%d %H:%M:%S'))
exifDf['day'] = exifDf.datetime.apply(lambda x : x.day)
exifDf['hour'] = exifDf.datetime.apply(lambda x : x.hour)
exifDf.drop(['size','datetime'],1,inplace=True)
exifDf.head()


    

    
Out[36]:






  

    

      

      
arousal
      
contrast
      
dominance
      
height
      
hsv_itten_std_h
      
hsv_itten_std_s
      
hsv_itten_std_v
      
lat
      
long
      
orientation
      
pleasure
      
symmetry
      
width
      
day
      
hour
    
  
  

    

      
1
      
0.0411613
      
2.44883
      
0.42617
      
4000
      
145133
      
184810
      
225483
      
-1.3514
      
36.8002
      
1
      
0.366162
      
6.15084
      
6000
      
1
      
14
    
    

      
10
      
-0.0677241
      
1.4738
      
0.500873
      
4000
      
105719
      
140171
      
133193
      
-1.36709
      
36.782
      
1
      
0.441554
      
11.4251
      
6000
      
1
      
15
    
    

      
100
      
0.0107685
      
2.27182
      
0.445344
      
4000
      
102122
      
139804
      
189860
      
-1.37325
      
36.8003
      
1
      
0.385749
      
8.83975
      
6000
      
1
      
16
    
    

      
1000
      
0.0267916
      
2.2988
      
0.446958
      
3072
      
100419
      
106270
      
146868
      
-1.34968
      
36.806
      
1
      
0.385603
      
10.8675
      
4608
      
1
      
17
    
    

      
1001
      
0.0131469
      
2.27636
      
0.445515
      
3072
      
107165
      
112807
      
144619
      
-1.34968
      
36.806
      
1
      
0.385667
      
10.9425
      
4608
      
1
      
17
    
  

In [ ]:

    

trace1 = go.Histogram(
        x=exifDf['height']
    )

trace2 = go.Histogram(
        x=exifDf['width']
    )

trace3 = go.Histogram(
        x=exifDf['lat']
    )

trace4 = go.Histogram(
        x=exifDf['long']
    )

trace5 = go.Histogram(
        x=exifDf['orientation']
    )

trace6 = go.Histogram(
        x=exifDf['day']
    )

trace7 = go.Histogram(
        x=exifDf['hour']
    )

trace8 = go.Histogram(
        x=exifDf['pleasure']
    )

trace9 = go.Histogram(
        x=exifDf['arousal']
    )

trace10 = go.Histogram(
        x=exifDf['dominance']
    )

trace11 = go.Histogram(
        x=exifDf['contrast']
    )

trace12 = go.Histogram(
        x=exifDf['hsv_itten_std_h']
    )

trace13 = go.Histogram(
        x=exifDf['hsv_itten_std_s']
    )

trace14 = go.Histogram(
        x=exifDf['hsv_itten_std_v']
    )

trace15 = go.Histogram(
        x=exifDf['symmetry']
    )


fig = tools.make_subplots(rows=8, cols=2, subplot_titles=('height','width','lat','long','orientation','day','hour','pleasure','arousal','dominance','contrast','hsv_itten_std_h','hsv_itten_std_s','hsv_itten_std_v','symmetry'))

fig.append_trace(trace1, 1, 1)
fig.append_trace(trace2, 1, 2)
fig.append_trace(trace3, 2, 1)
fig.append_trace(trace4, 2, 2)
fig.append_trace(trace5, 3, 1)
fig.append_trace(trace6, 3, 2)
fig.append_trace(trace7, 4, 1)
fig.append_trace(trace8, 4, 2)
fig.append_trace(trace9, 5, 1)
fig.append_trace(trace10, 5, 2)
fig.append_trace(trace11, 6, 1)
fig.append_trace(trace12, 6, 2)
fig.append_trace(trace13, 7, 1)
fig.append_trace(trace14, 7, 2)
fig.append_trace(trace15, 8, 1)


fig['layout'].update(showlegend=False, height=1000, width=1000, title='Data distributions')
plot(fig, filename='../FinalResults/Distributions_BeautyFeatures_GZC.html')


    

In [ ]:

    

from numpy import arange,array,ones
from scipy import stats


    

In [37]:

    

resultsDf = pd.DataFrame.from_csv("../FinalResults/resultsExpt2RankList_Tags.csv")

resultsDf = pd.DataFrame(resultsDf['Proportion'])
resultsDict = resultsDf.to_dict(orient="index")

expt2Results = {}

for gid in resultsDict:
    expt2Results[str(gid)] = exifJsonObj[str(gid)]
    expt2Results[str(gid)].update(resultsDict[gid])


    

In [38]:

    

expt2ResultsDf = pd.DataFrame(expt2Results).transpose()

expt2ResultsDf['datetime'] = expt2ResultsDf['datetime'].apply(lambda x : datetime.strptime(x,'%Y-%m-%d %H:%M:%S'))
expt2ResultsDf['day'] = expt2ResultsDf.datetime.apply(lambda x : x.day)
expt2ResultsDf['hour'] = expt2ResultsDf.datetime.apply(lambda x : x.hour)
expt2ResultsDf.drop(['size','datetime'],1,inplace=True)
expt2ResultsDf.head()


    

    
Out[38]:






  

    

      

      
Proportion
      
arousal
      
contrast
      
dominance
      
height
      
hsv_itten_std_h
      
hsv_itten_std_s
      
hsv_itten_std_v
      
lat
      
long
      
orientation
      
pleasure
      
symmetry
      
width
      
day
      
hour
    
  
  

    

      
10
      
20
      
-0.0677241
      
1.4738
      
0.500873
      
4000
      
105719
      
140171
      
133193
      
-1.36709
      
36.782
      
1
      
0.441554
      
11.4251
      
6000
      
1
      
15
    
    

      
1000
      
90
      
0.0267916
      
2.2988
      
0.446958
      
3072
      
100419
      
106270
      
146868
      
-1.34968
      
36.806
      
1
      
0.385603
      
10.8675
      
4608
      
1
      
17
    
    

      
1003
      
20
      
0.00380291
      
1.61078
      
0.534058
      
3072
      
116605
      
145911
      
182645
      
-1.34965
      
36.8062
      
1
      
0.463471
      
9.66617
      
4608
      
1
      
17
    
    

      
1005
      
80
      
-0.049412
      
1.75693
      
0.55702
      
3072
      
102427
      
140823
      
132426
      
-1.34965
      
36.8062
      
1
      
0.488551
      
12.6299
      
4608
      
1
      
17
    
    

      
101
      
20
      
0.0550115
      
2.42374
      
0.444083
      
4000
      
142995
      
193383
      
250416
      
-1.37325
      
36.8003
      
1
      
0.380382
      
6.21572
      
6000
      
1
      
16
    
  

In [ ]:

    

def linearRegressPlot(resultDf,xFtr,yStr):
    xi = np.array(resultDf[xFtr])
    y = list(resultDf[yStr])
    slope, intercept, r_value, p_value, std_err = stats.linregress(xi,y)
    line = slope*xi+intercept
    layout= go.Layout(
            title= "Linear line fit for regression using %s" %xFtr,
            showlegend=False,
            xaxis= dict(
                title= xFtr,
                ticklen= 5,
                zeroline= True,
                gridwidth= 2
            ),
            yaxis=dict(
                title= yStr,
                ticklen= 5,
                gridwidth= 2,
                #range=range
            )
        )

    trace1 = go.Scatter(
                x = xi,
                y = y,
                mode = 'markers'
        )

    trace2 = go.Scatter(
                x=xi, 
                y=line, 
                mode='lines',
                name='Fit'
                      )

    data = [trace1,trace2]

    fig = dict(data=data,layout=layout)
    figMain = py.iplot(fig,filename= str("linear_fit_%s" %xFtr))
    print("r_value, p_value" ,(r_value,p_value))
    print(figMain.embed_code)

linearRegressPlot(expt2ResultsDf,'symmetry','Proportion')


    

In [ ]:

    

xi = np.array(expt2ResultsDf['height'])
y = list(expt2ResultsDf['Proportion'])
slope, intercept, r_value, p_value, std_err = stats.linregress(xi,y)
slope, intercept, r_value, p_value, std_err


    

In [ ]:

    

exifDf.max()


    

In [ ]:

    




    

In [ ]:

    

import importlib

import ExtractBtyFtrs as EBty
importlib.reload(EBty)


    

In [ ]:

    

EBty.createFtrFile("../FinalResults/resultsExpt2RankList_Tags.csv","../data/GZC_exifs_beauty_full.json","../data/BeautyFtrVector_GZC.csv")


    

In [ ]:

    

resultsDf = pd.DataFrame.from_csv("../FinalResults/resultsExpt2RankList_Tags.csv")
resultsDf.head()


    

In [ ]:

    




    

In [67]:

    

from sklearn.model_selection import train_test_split
import ClassiferHelperAPI as CH
import RegressionCapsuleClass as RgrCls
import pandas as pd
import importlib
importlib.reload(RgrCls)

from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
import plotly.graph_objs as go
import cufflinks as cf
cf.go_offline()


    

In [2]:

    

inpData = pd.DataFrame.from_csv("../data/BeautyFtrVector_GZC.csv")
inpData.head()


    

    
Out[2]:






  

    

      

      
Proportion
      
arousal
      
contrast
      
dominance
      
height
      
hsv_itten_std_h
      
hsv_itten_std_s
      
hsv_itten_std_v
      
lat
      
long
      
orientation
      
pleasure
      
symmetry
      
width
      
day
      
hour
    
  
  

    

      
10
      
20.0
      
-0.067724
      
1.473800
      
0.500873
      
4000
      
105718.831503
      
140170.630774
      
133192.928522
      
-1.367088
      
36.781978
      
1
      
0.441554
      
11.425084
      
6000
      
1
      
15
    
    

      
1000
      
90.0
      
0.026792
      
2.298800
      
0.446958
      
3072
      
100418.690900
      
106270.090353
      
146867.861046
      
-1.349683
      
36.806025
      
1
      
0.385603
      
10.867455
      
4608
      
1
      
17
    
    

      
1003
      
20.0
      
0.003803
      
1.610780
      
0.534058
      
3072
      
116605.125589
      
145911.415673
      
182644.571576
      
-1.349651
      
36.806162
      
1
      
0.463471
      
9.666165
      
4608
      
1
      
17
    
    

      
1005
      
80.0
      
-0.049412
      
1.756933
      
0.557020
      
3072
      
102427.151569
      
140822.729057
      
132426.326397
      
-1.349651
      
36.806162
      
1
      
0.488551
      
12.629949
      
4608
      
1
      
17
    
    

      
101
      
20.0
      
0.055012
      
2.423743
      
0.444083
      
4000
      
142994.891693
      
193382.957863
      
250415.909007
      
-1.373255
      
36.800266
      
1
      
0.380382
      
6.215719
      
6000
      
1
      
16
    
  

In [ ]:

    

inpData = inpData[(inpData['Proportion'] >= 80.0) | (inpData['Proportion'] <= 20.0)]
inpData['TARGET'] = np.where(inpData['Proportion'] >= 80.0, 1, 0)
inpData.drop(['Proportion'],1,inplace=True)


    

In [3]:

    

y = inpData['Proportion']
inpData.drop(['Proportion'],1,inplace=True)


    

In [1]:

    




    

In [17]:

    

train_x, test_x, train_y, test_y = CH.train_test_split(inpData, y, test_size = 0.2)
rgr = CH.getLearningAlgo('elastic_net',{'fit_intercept':True})
    
rgrObj = RgrCls.RegressionCapsule(rgr,'elastic_net',0.8,train_x,train_y,test_x,test_y)


    

In [18]:

    

rgrObj.runRgr(removeOutliers=False)


    

In [19]:

    

rgrObj.abserr, rgrObj.sqerr


    

    
Out[19]:





(23.611381944920133, 796.88386165148518)

In [20]:

    

x_data = rgrObj.preds
y_data = rgrObj.residues


    

In [24]:

    

# Create a trace
trace = go.Scatter(
    x = x_data,
    y = y_data,
    mode = 'markers'
)

data = [trace]

# Plot and embed in ipython notebook!
a = py.iplot(data, filename='basic-scatter')


    

In [26]:

    

a.embed_code


    

    
Out[26]:





'<iframe id="igraph" scrolling="no" style="border:none;" seamless="seamless" src="https://plot.ly/~kamblenoopur/103.embed" height="525px" width="100%"></iframe>'

In [ ]:

    

rgrObj.fit(train_x, train_y)


    

In [ ]:

    

from sklearn.metrics import mean_absolute_error, mean_squared_error


    

In [ ]:

    

mean_squared_error(test_y, preds), mean_absolute_error(test_y, preds)


    

In [ ]:

    

mean_squared_error(test_y, preds), mean_absolute_error(test_y, preds)


    

In [7]:

    

inpData.head()


    

    
Out[7]:






  

    

      

      
arousal
      
contrast
      
dominance
      
height
      
hsv_itten_std_h
      
hsv_itten_std_s
      
hsv_itten_std_v
      
lat
      
long
      
orientation
      
pleasure
      
symmetry
      
width
      
day
      
hour
    
  
  

    

      
10
      
-0.067724
      
1.473800
      
0.500873
      
4000
      
105718.831503
      
140170.630774
      
133192.928522
      
-1.367088
      
36.781978
      
1
      
0.441554
      
11.425084
      
6000
      
1
      
15
    
    

      
1000
      
0.026792
      
2.298800
      
0.446958
      
3072
      
100418.690900
      
106270.090353
      
146867.861046
      
-1.349683
      
36.806025
      
1
      
0.385603
      
10.867455
      
4608
      
1
      
17
    
    

      
1003
      
0.003803
      
1.610780
      
0.534058
      
3072
      
116605.125589
      
145911.415673
      
182644.571576
      
-1.349651
      
36.806162
      
1
      
0.463471
      
9.666165
      
4608
      
1
      
17
    
    

      
1005
      
-0.049412
      
1.756933
      
0.557020
      
3072
      
102427.151569
      
140822.729057
      
132426.326397
      
-1.349651
      
36.806162
      
1
      
0.488551
      
12.629949
      
4608
      
1
      
17
    
    

      
101
      
0.055012
      
2.423743
      
0.444083
      
4000
      
142994.891693
      
193382.957863
      
250415.909007
      
-1.373255
      
36.800266
      
1
      
0.380382
      
6.215719
      
6000
      
1
      
16
    
  

In [1]:

    

import json
import pandas as pd
from datetime import datetime


    

In [12]:

    

with open("../data/GZC_exifs_beauty_full.json") as jsonObj:
    jsonDict = json.load(jsonObj)
df = pd.DataFrame.from_dict(jsonDict).transpose()
df['datetime'] = df['datetime'].apply(lambda x : datetime.strptime(x,'%Y-%m-%d %H:%M:%S'))
df['day'] = df.datetime.apply(lambda x : x.day)
df['hour'] = df.datetime.apply(lambda x : x.hour)
df.drop(['size','datetime'],1,inplace=True)
# df.head()
df.to_csv("../data/GZC_exifs_beauty_full.csv")


    

In [5]:

    

len(df)


    

    
Out[5]:





9406

In [161]:

    

import PopulationEstimatorFromClf as PE
import importlib
importlib.reload(PE)

regrArgs = {'linear' : {'fit_intercept' : True},
            'ridge' : {'fit_intercept' : True},
            'lasso' : {'fit_intercept' : True},
            'svr' : {'fit_intercept' : True},
            'dtree_regressor' : {'fit_intercept' : True}}

methArgs = {'dummy' : {'strategy' : 'most_frequent'},
            'bayesian' : {'fit_prior' : True},
            'logistic' : {'penalty' : 'l2'},
            'svm' : {'kernel' : 'rbf','probability' : True},
            'dtree' : {'criterion' : 'entropy'},
            'random_forests' : {'n_estimators' : 10 },
            'ada_boost' : {'n_estimators' : 50 }}

inExifFl,inGidAidMapFl,inAidFtrFl = "../data/imgs_exif_data_full.json","../data/full_gid_aid_map.json","../data/full_aid_features.json"


    

In [126]:

    

# obj = PE.trainLearningObj("../data/BeautyFtrVector_GZC_Expt2.csv",
#                          "../data/GZC_exifs_beauty_full.csv",
#                          'logistic',
#                          'beauty',
#                           None,
#                           methArgs,
#                           True
#                          )

# obj, res = PE.trainTestRgrs("../FinalResults/ImgShrRnkListWithTags.csv",
#                          "../data/full_gid_aid_ftr_agg.csv",
#                          'linear',
#                          'non_sparse',
#                           None,
#                           regrArgs
#                          )
obj, res = PE.trainTestRgrs("../data/BeautyFtrVector_GZC_Expt2.csv",
                         "../data/GZC_exifs_beauty_full.csv",
                         'linear',
                         'beauty',
                          None,
                          regrArgs
                         )


    

    




Number of outliers identified: 1
9405 9405

In [124]:

    

a, results = PE.trainTestClf("../data/BeautyFtrVector_GZC_Expt2.csv",
                         "../data/GZC_exifs_beauty_full.csv",
                             'logistic',
                             'beauty',
                              None,
                              methArgs
                            )


    

In [125]:

    

res


    

    
Out[125]:





{'8287': 49.216683559356369,
 '8259': 51.854799518005493,
 '806': 40.976623978089968,
 '2029': 49.619708806657179,
 '2089': 46.91469371258566,
 '8764': 53.074001804819886,
 '4206': 44.781509699096588,
 '3950': 46.439990824972845,
 '7793': 48.08013391376808,
 '1205': 55.807458747326031,
 '1575': 56.469391803409394,
 '2362': 53.549256487678107,
 '3621': 54.431403786405959,
 '5142': 64.005551625247108,
 '8047': 46.918979677014065,
 '1311': 56.47662729294936,
 '7425': 45.055601212925907,
 '6991': 50.176266220033874,
 '265': 53.703759804079851,
 '7655': 41.568261282006048,
 '1865': 49.081933678705269,
 '2407': 62.310867355902523,
 '7022': 54.519277289121412,
 '1239': 49.293419965751269,
 '8955': 51.701047107778535,
 '2380': 55.031108928605136,
 '3563': 43.178152964248142,
 '8027': 47.497593612958433,
 '2850': 51.847676735639205,
 '4329': 40.79933072318066,
 '276': 58.998472733061071,
 '8455': 46.22894308655011,
 '1075': 51.401982340634994,
 '4811': 42.097632620371613,
 '8783': 47.513556972753207,
 '3450': 60.045920481732907,
 '1941': 51.99874905592808,
 '987': 26.501078139184106,
 '732': 45.43411124969878,
 '6672': 34.109162979233702,
 '8688': 48.55274663928742,
 '9234': 52.747798050868298,
 '1765': 50.026341509826047,
 '7407': 52.790771995265999,
 '4509': 40.843438889356527,
 '6898': 59.965155708843604,
 '1900': 51.479709649809976,
 '6576': 50.756857072902193,
 '3990': 62.316809655570488,
 '104': 48.018014364603232,
 '5598': 54.629779228309367,
 '1003': 46.082826733520903,
 '4637': 47.546254776110715,
 '113': 58.704071909474536,
 '7269': 42.902331247326281,
 '6218': 38.919911980297016,
 '5222': 51.567459255156351,
 '5190': 53.487921612102753,
 '1666': 56.897039030501858,
 '8705': 49.360685893765037,
 '8333': 53.088128926331592,
 '4798': 40.806353447294462,
 '2519': 71.137023238937957,
 '282': 52.95088105001858,
 '5466': 50.292724845006546,
 '2456': 52.794642586256288,
 '5722': 52.527840693393983,
 '2995': 54.748858531909264,
 '9361': 56.548408669550028,
 '79': 47.899982877724838,
 '7257': 50.508477768410245,
 '6572': 42.218024396704877,
 '1754': 53.796699948922381,
 '3444': 54.650575570917596,
 '8153': 42.518890402856734,
 '407': 49.308739431857845,
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 '5105': 57.430368269745671,
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 '3718': 57.787135040135311,
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 '1267': 54.113066280834964,
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 '1433': 65.401291432986142,
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 '4166': 54.098779450802716,
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 '6699': 45.49941156768255,
 '357': 49.859934998041922,
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 '6822': 55.033453564107788,
 '325': 50.429181640730974,
 '2657': 47.386663918011401,
 '2072': 41.822496266908146,
 '3840': 48.820340063380478,
 '7428': 48.026888636467334,
 '5783': 54.750241395936136,
 '7197': 42.973263421753941,
 '4339': 48.376853358253356,
 '1720': 53.78713706831924,
 '1960': 49.918744527846755,
 '3938': 46.959434343100277,
 '8773': 49.417271561851621,
 '604': 49.83541674201274,
 '7287': 40.581187576465979,
 '3133': 52.390798647038437,
 '4360': 16.94767433345525,
 '7561': 45.265164613549778,
 '1711': 64.637114997236907,
 '7211': 42.131820975864287,
 '842': 48.222340928856816,
 '1816': 43.046134922766711,
 '4488': 30.955548878987933,
 '6183': 51.113005946206826,
 '997': 42.142505201969151,
 '7908': 44.65753199553464,
 '7979': 48.691147949807444,
 '7940': 42.682433776216548,
 '4446': 31.430056423101309,
 '583': 50.540302261770869,
 '8681': 46.824610329375346,
 '1921': 45.280056897676289,
 '5821': 54.766958586123877,
 '466': 49.901319944072355,
 '4036': 53.12036252768975,
 '5857': 59.057480901725029,
 '8652': 48.165002910141126,
 '6203': 48.822881927890883,
 '5851': 56.876440061627392,
 '2682': 51.866561954048386,
 '533': 54.755648503758813,
 '8127': 47.135222933874168,
 '170': 50.350121345682318,
 '4973': 46.837393119268519,
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 '3555': 47.609526061831552,
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 '413': 50.046058525181067,
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 '3559': 43.800997178954688,
 '5375': 44.628063885125123,
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 '1393': 53.115388357372467,
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 '8337': 58.896420450455651,
 '2661': 48.81478957026836,
 '7903': 54.028775960092858,
 ...}

In [127]:

    

PE.kSharesPerContribAfterCoinFlip(res,inExifFl,inGidAidMapFl,inAidFtrFl, lambda : 70)


    

    
Out[127]:





{'all': 3252.0, 'giraffes': 195.8, 'zebras': 3063.315789473684}

In [118]:

    

len(obj.preds)


    

    
Out[118]:





6469

In [162]:

    

df, titleSuffix = PE.buildErrPlots('clf')


    

In [156]:

    

df, titleSuffix = PE.buildErrPlots('clf')
attribTypes = ['beauty']
figs = []
for attrib in attribTypes:
    attribCol = [col for col in df.columns if attrib in col]
    attribDf = df[attribCol]
    titleAttrib = "All %s %s" %(attrib,titleSuffix)
    figs.append(attribDf.iplot(kind='line',title=titleAttrib))

figCodes = [fig.embed_code for fig in figs]


    

In [151]:

    

titleSuffix


    

    
Out[151]:





'classifiers top k choices'

In [157]:

    

figCodes


    

    
Out[157]:





['<iframe id="igraph" scrolling="no" style="border:none;" seamless="seamless" src="https://plot.ly/~smenon8/497.embed" height="525px" width="100%"></iframe>']

In [167]:

    

for col in df.columns:
    if 'all' in col:
        df[str(col+'_err')] = (3620 - df[col]) / 36.20


    

In [168]:

    

df.head()


    

    
Out[168]:






  

    

      

      
all_bayesian_beauty
      
giraffes_bayesian_beauty
      
zebras_bayesian_beauty
      
all_logistic_beauty
      
giraffes_logistic_beauty
      
zebras_logistic_beauty
      
all_svm_beauty
      
giraffes_svm_beauty
      
zebras_svm_beauty
      
all_dtree_beauty
      
...
      
zebras_random_forests_beauty
      
all_ada_boost_beauty
      
giraffes_ada_boost_beauty
      
zebras_ada_boost_beauty
      
all_bayesian_beauty_err
      
all_logistic_beauty_err
      
all_svm_beauty_err
      
all_dtree_beauty_err
      
all_random_forests_beauty_err
      
all_ada_boost_beauty_err
    
    

      
num_images
      

      

      

      

      

      

      

      

      

      

      

      

      

      

      

      

      

      

      

      

      

    
  
  

    

      
2
      
649.000000
      
8.0
      
1071.0
      
649.000000
      
8.0
      
1071.0
      
649.000000
      
8.0
      
1071.0
      
649.000000
      
...
      
1071.0
      
649.000000
      
8.0
      
1071.0
      
82.071823
      
82.071823
      
82.071823
      
82.071823
      
82.071823
      
82.071823
    
    

      
3
      
1232.500000
      
12.0
      
2044.0
      
1232.500000
      
12.0
      
2044.0
      
1232.500000
      
12.0
      
2044.0
      
1232.500000
      
...
      
2044.0
      
1232.500000
      
12.0
      
2044.0
      
65.953039
      
65.953039
      
65.953039
      
65.953039
      
65.953039
      
65.953039
    
    

      
4
      
2160.000000
      
14.0
      
3666.0
      
2160.000000
      
14.0
      
3666.0
      
2160.000000
      
14.0
      
3666.0
      
2160.000000
      
...
      
3666.0
      
2160.000000
      
14.0
      
3666.0
      
40.331492
      
40.331492
      
40.331492
      
40.331492
      
40.331492
      
40.331492
    
    

      
5
      
3219.500000
      
17.0
      
5520.0
      
3219.500000
      
17.0
      
5520.0
      
3219.500000
      
17.0
      
5520.0
      
3219.500000
      
...
      
5520.0
      
3219.500000
      
17.0
      
5520.0
      
11.063536
      
11.063536
      
11.063536
      
11.063536
      
11.063536
      
11.063536
    
    

      
6
      
3107.333333
      
20.0
      
4002.0
      
3107.333333
      
20.0
      
4002.0
      
3107.333333
      
20.0
      
4002.0
      
3107.333333
      
...
      
4002.0
      
3107.333333
      
20.0
      
4002.0
      
14.162063
      
14.162063
      
14.162063
      
14.162063
      
14.162063
      
14.162063
    
  

5 rows × 24 columns

In [1]:

    

import pandas as pd
import json
import re
from datetime import datetime


    

In [2]:

    

with open("../data/Flickr_EXIF_full.json", "r") as exiffl:
    d = json.load(exiffl)
df_exif = pd.DataFrame.from_dict(d).transpose()


    

In [10]:

    

with open("../data/beautyFeatures_FlickrExtracts.json", "r") as btyFl:
    dBty = json.load(btyFl)

# dBty_mod = {}
# for img in dBty.keys():
#     mod_img = re.findall(r'([0-9]*)_.*',img)[0]
#     dBty_mod[mod_img] = dBty[img]

df_bty = pd.DataFrame.from_dict(dBty).transpose()


    

In [15]:

    

df_exif.reset_index(inplace=True)
df_bty.reset_index(inplace=True)


    

In [17]:

    

df = pd.DataFrame.merge(df_exif, df_bty)
df.index = df['index']


    

In [19]:

    

df['date'] = df['date'].apply(lambda x : datetime.strptime(str(x),'%Y-%m-%d %H:%M:%S'))
df['day'] = df.date.apply(lambda x : x.day)
df['hour'] = df.date.apply(lambda x : x.hour)
df.drop(['index', 'date'],1,inplace=True)


    

In [20]:

    

df.to_csv("../data/Flickr_Scrapes_Ftrs.csv")


    

In [1]:

    

import PopulationEstimatorAPI as PE


    

In [2]:

    

train_fl = "../data/BeautyFtrVector_GZC_Expt2.csv"
test_fl = "../data/Flickr_Scrapes_Ftrs.csv"
meth = 'lasso'
attrib = 'beauty'
methArgs = {'linear' : {'fit_intercept' : True},
            'ridge' : {'fit_intercept' : True},
            'lasso' : {'fit_intercept' : True},
            'elastic_net' : {'fit_intercept' : True},
            'svr' : {'fit_intercept' : True},
            'dtree_regressor' : {'fit_intercept' : True}}
methObj,predResults = PE.trainTestRgrs(train_fl,
                                test_fl,
                                meth,
                                attrib,
                                infoGainFl="../data/infoGainsExpt2.csv",
                                methArgs = methArgs
                                )


    

    




Number of outliers identified: 0
1701 1701

In [3]:

    

max(predResults.values()), min(predResults.values())


    

    
Out[3]:





(78.140077977527866, 31.369773817297357)

In [9]:

    

imgs = list(methObj.test_x.index)


    

In [11]:

    

with open("../data/fileURLS.dat") as urlFls:
    urls = urlFls.read().split("\n")


    

In [28]:

    

flUrlDict = {}
for img in imgs:
    for url in urls:
        if str(img) in url:
            flUrlDict[str(img)] = url


    

In [29]:

    

with open("Flickr_FL_URL_map.json", "w") as fl:
    json.dump(flUrlDict, fl, indent = 4)


    

In [26]:

    

df = pd.DataFrame.from_csv("../data/BeautyFtrVector_GZC_Expt2.csv")


    

In [23]:

    

def rotation_to_orientation(theta):
    if theta == 0:
        return 1
    elif theta == 90:
        return 8
    else:
        return 6


    

In [24]:

    

df['orientation'] = df['rotation'].apply(rotation_to_orientation)


    

In [27]:

    

df.drop(['lat', 'long', 'day', 'rotation'], 1, inplace=True)


    

    




---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-27-be0d7979472a> in <module>()
----> 1 df.drop(['lat', 'long', 'day', 'rotation'], 1, inplace=True)
      2 df.to_csv("/tmp/testing_fl_flickr.csv")

/Users/sreejithmenon/anaconda/lib/python3.5/site-packages/pandas/core/generic.py in drop(self, labels, axis, level, inplace, errors)
   1875                 new_axis = axis.drop(labels, level=level, errors=errors)
   1876             else:
-> 1877                 new_axis = axis.drop(labels, errors=errors)
   1878             dropped = self.reindex(**{axis_name: new_axis})
   1879             try:

/Users/sreejithmenon/anaconda/lib/python3.5/site-packages/pandas/indexes/base.py in drop(self, labels, errors)
   3049             if errors != 'ignore':
   3050                 raise ValueError('labels %s not contained in axis' %
-> 3051                                  labels[mask])
   3052             indexer = indexer[~mask]
   3053         return self.delete(indexer)

ValueError: labels ['lat' 'long' 'day' 'rotation'] not contained in axis

In [28]:

    

df.to_csv("/tmp/testing_fl_flickr.csv")


    

In [ ]: