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Edit and run 





In [ ]:



    






    











In [2]:



    


import json, pandas as pd,re
import DeriveFinalResultSet as DRS
import cufflinks as cf # this is necessary to link pandas to plotly
cf.go_offline()
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
import plotly.plotly as py
import ClassiferHelperAPI as CH, PopulationEstimatorAPI as PE
import importlib, re
importlib.reload(CH)
importlib.reload(DRS)
importlib.reload(PE)
import MarkRecapHelper as MR
importlib.reload(MR)
import JobsMapResultsFilesToContainerObjs as ImageMap
importlib.reload(ImageMap)
import plotly.graph_objs as go



    


















    
















    























In [3]:



    


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



    











In [88]:



    


df = CH.construct_feature_vctr_fl("../data/GZC_exifs_beauty_full.json", None, 
                            "../data/full_gid_aid_map.json",
                            "../data/full_aid_features.json",
                            "../feature_files/GZC_FEATURE_VECTOR.csv",
                            ['arousal', 'contrast', 'dominance', 'pleasure', 'symmetry', 
                               'height', 'width',
                               'hsv_itten_std_h', 'hsv_itten_std_s', 'hsv_itten_std_v', 
                               'lat', 'long',
                               'orientation', 
                               'day', 'hour',
                               'has_zebra'])



    











In [78]:



    


df = CH.construct_feature_vctr_fl("../data/GGR_bty_exif_combined.json", 
                                  None, 
                            "../data/ggr_uuid_annot_uuid_map.json",
                            "../data/ggr_annot_uuid_ftr_map.json",
                            "../feature_files/GGR_FEATURE_VECTOR.csv",
                            ['arousal', 'contrast', 'dominance', 'pleasure', 'symmetry', 
                               'height', 'width',
                               'hsv_itten_std_h', 'hsv_itten_std_s', 'hsv_itten_std_v', 
                               'lat', 'long',
                               'orientation', 
                               'day', 'hour',
                               'has_zebra'])



    











In [5]:



    


PE.runSyntheticExpts(False, ['elastic_net'], ['beauty'], range(20,31), regrArgs)



    


















    






Starting to run elastic_net on test data
Attribute Selection Method : beauty
Number of outliers identified: 0
6524 6524
Starting population estimation experiments
Population estimation experiments complete










    
Out[5]:










  
    

      
      all
      giraffes
      zebras
      zebra_confidence
      Randomized_giraffe
      Randomized_all
      Randomized_zebras
    

    

      num_images
      
      
      
      
      
      
      
    

  
  
    

      20
      2340.000000
      80.00
      4737.871541
      2571.400000
      126.0
      3160.8
      3443.181818
    

    

      21
      2491.071429
      80.00
      5098.874901
      2763.200000
      126.0
      3160.8
      3443.181818
    

    

      22
      2591.714286
      81.25
      5324.877615
      2883.600000
      126.0
      3160.8
      3443.181818
    

    

      23
      2750.642857
      81.25
      5702.978289
      3082.000000
      126.0
      3160.8
      3443.181818
    

    

      24
      2899.285714
      97.50
      5992.607532
      3236.400000
      126.0
      3160.8
      3443.181818
    

    

      25
      3134.142857
      106.50
      6535.942003
      3521.100000
      126.0
      3160.8
      3443.181818
    

    

      26
      3160.800000
      126.00
      6370.545113
      3443.181818
      126.0
      3160.8
      3443.181818
    

    

      27
      3135.000000
      126.00
      6198.694043
      3364.666667
      126.0
      3160.8
      3443.181818
    

    

      28
      3166.117647
      144.00
      6108.666150
      3323.076923
      126.0
      3160.8
      3443.181818
    

    

      29
      3186.333333
      144.00
      6071.118523
      3309.285714
      126.0
      3160.8
      3443.181818
    

    

      30
      3421.944444
      166.50
      6529.317761
      3548.428571
      126.0
      3160.8
      3443.181818
    

  




















In [8]:



    


df_logistic_gzc = PE.runSyntheticExpts(True, ['logistic'], ['beauty'], range(5,60), clfArgs)



    


















    






Starting to run logistic on test data
Attribute Selection Method : beauty
Starting population estimation experiments
280










    






/Users/sreejithmenon/Google Drive/CodeBase/AWESOME/script/MarkRecapHelper.py:94: UserWarning:

There are no recaptures for this case.











    






0
336
0
392
0
448
0
503
0
557
610
661
712
762
811
860
909
958
1006
1053
1099
1145
1191
1237
1283
1328
1372
1415
1457
1499
1541
1583
1625
1667
1708
1749
1790
1830
1870
1910
1949
1988
2027
2066
2105
2144
2183
2222
2261
2299
2337
2373
2409
2445
2480
2515
2550
2585
2619
Population estimation experiments complete
1644

















In [221]:



    


PE.runSyntheticExpts(True, ['logistic'], ['beauty'], range(25,26), clfArgs)



    


















    






Starting to run logistic on test data
Attribute Selection Method : beauty
Starting population estimation experiments
mode is GZC
1283
mode is GZC
0










    






/Users/sreejithmenon/Google Drive/CodeBase/AWESOME/script/MarkRecapHelper.py:94: UserWarning:

There are no recaptures for this case.











    






mode is GZC
mode is GZC
0
mode is GZC
mode is GZC
0
Population estimation experiments complete
mode is GZC
1283
mode is GZC
0
mode is GZC
mode is GZC
0
mode is GZC
mode is GZC
0










    
Out[221]:










  
    

      
      all
      giraffes
      zebras
      zebra_confidence
      Randomized_giraffe
      Randomized_all
      Randomized_zebras
    

    

      num_images
      
      
      
      
      
      
      
    

  
  
    

      25
      NaN
      NaN
      0.0
      NaN
      None
      None
      None
    

  




















In [4]:



    


clfArgs = {'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 }}

regrArgs = {'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}}



    











In [131]:



    


contrib_extractor = lambda x : re.findall(r'GGR,([0-9]+,[A-Z]),.*', x)[0]
with open(inExifFl, "r") as exif_fl:
    exif_obj = json.load(exif_fl)
gidContribDct = {gid_uuid : [contrib_extractor(exif_obj[gid_uuid]['contributor'])] for gid_uuid in exif_obj.keys()}



    











In [132]:



    


contrib_extractor(exif_obj['a7c6379f-ab06-b409-9836-3f696c73e86e']['contributor'])



    


















    
Out[132]:








'7,A'

















In [228]:



    


try:
    assert MR.MODE == MODE
except AssertionError as e:
    print("Mode mismatch")
    sys.exit(-2)



    


















    






Mode mismatch










    






An exception has occurred, use %tb to see the full traceback.

SystemExit: -2










    






To exit: use 'exit', 'quit', or Ctrl-D.

















In [227]:



    


MODE = 'blah'
import sys



    











In [208]:



    


if len(a[0]):
    lasdas



    











In [26]:



    


df_logistic_gzc['zebra_error'] = (df_logistic_gzc['zebras'] - 2673) / 26.73



    











In [14]:



    


def buildErrPlots(clfOrRgr, thresholdMeth=False, randomShare=False):
    if clfOrRgr == 'clf':
        algTypes = ['logistic','ada_boost']
    else:
        algTypes = ['linear','elastic_net']
    attribTypes = [ 'beauty']
    flNms = [str(alg + "_" + attrib) for alg in algTypes for attrib in attribTypes]
    print(flNms)
    if thresholdMeth:
        suffix = "_thresholded.csv"
        hdr = "threshold"
        if clfOrRgr == 'clf':
            titleSuffix = "classifiers thresholded"
        else:
            titleSuffix = "regressors thresholded"
    else:
        hdr = "num_images"
        if randomShare:
            suffix = "_kSharesRandom.csv"
            if clfOrRgr == 'clf':
                titleSuffix = "classifiers random choices"
            else:
                titleSuffix = "regressors random choices"
        else:
            suffix = "_bottom_kShares.csv"
            if clfOrRgr == 'clf':
                titleSuffix = "classifiers top k choices"
            else:
                titleSuffix = "regressors top k choices"
    print(suffix)
    df = pd.DataFrame.from_csv(str("../FinalResults/"+flNms[0]+suffix)).reset_index()
    df.columns = list(map(lambda x : str(x + "_" + flNms[0]) if x != hdr else x,list(df.columns)))
    for i in range(1,len(flNms)):
        df1 = pd.DataFrame.from_csv(str("../FinalResults/"+flNms[i]+suffix)).reset_index()
        df1.columns = list(map(lambda x : str(x + "_" + flNms[i]) if x != hdr else x,list(df1.columns)))
        df = pd.DataFrame.merge(df,df1,on=hdr)

    df.index = df[hdr]
    df.drop([hdr],1,inplace=True)
    

    # calculate errors in estimation
    # % error = (predicted - actual) * 100 / actual
    for col in df.columns:
        if 'all' in col:
            df[str(col+'_err')] = (df[col] - 2673) / 36.20
        elif 'zebras' in col:
            df[str(col+'_err')] = (df[col] - 3000) / 30
        elif 'giraffes' in col:
            df[str(col+'_err')] = (df[col] - 123) / 1.23

    figs=[]
    errorCols = [col for col in df.columns if 'err' in col]
    df = df[errorCols]
    for col in errorCols:
        df[col][abs(df[col]) >= 100] = None
        
    return df
    for alg in algTypes:
        algCol = [col for col in df.columns if alg in col]
        algDf = df[algCol]
        titleAlg = "All %s %s" %(alg,titleSuffix)
        figs.append(algDf.iplot(kind='line',title=titleAlg))

    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]
    return figs #figCodes



    











In [15]:



    


df_top = buildErrPlots('rgr').reset_index()
df_random = buildErrPlots('rgr', randomShare=True).reset_index()
# df_random = buildErrPlots('rgr', thresholdMeth=True).reset_index()

# df.my_channel[df.my_channel >20000] = 0



    


















    






['linear_beauty', 'elastic_net_beauty']
_bottom_kShares.csv
['linear_beauty', 'elastic_net_beauty']
_kSharesRandom.csv

















In [16]:



    


df_bottom = buildErrPlots('rgr').reset_index()



    


















    






['linear_beauty', 'elastic_net_beauty']
_bottom_kShares.csv

















In [36]:



    


layout= go.Layout(
                    showlegend=True,
                    legend=dict(
                        x=0.5,
                        y=-0.0125,
                        font=dict(size=15)
                    ),
                    xaxis= dict(
                        title= 'K (number of images each contributor shares)',
                        ticklen= 5,
                        zeroline= True,
                        titlefont=dict(size=15),
                        tickfont=dict(size=15),
                    ),
                    yaxis=dict(
                    title= 'Percent Error',
                        ticklen= 5,
                        titlefont=dict(size=15),
                        tickfont=dict(size=15),
                        #range=range
                    )
                )

trace1 = go.Scatter(
                        x = list(df_top.num_images),
                        name = "Estimation Error using Linear Regression - Top K shared",
                        y = list(df_top.giraffes_linear_beauty_err),
                        opacity = 0.5,
                        # marker=dict(color='blue')

                )

trace2 = go.Scatter(
                        name = "Estimation Error using Elastic Net Regression - Top K shared",
                        x = list(df_top.num_images),
                        y = list(df_top.giraffes_elastic_net_beauty_err),
                        opacity = 1,
                        # marker=dict(color='red'),

                )

trace3 = go.Scatter(
                        x = list(df_bottom.num_images),
                        name = "Estimation Error using Linear Regression - Bottom K shared",
                        y = list(df_bottom.giraffes_linear_beauty_err),
                        opacity = 0.5,
                        # marker=dict(color='blue')

                )

trace4 = go.Scatter(
                        name = "Estimation Error using Elastic Net Regression - Bottom K shared",
                        x = list(df_bottom.num_images),
                        y = list(df_bottom.giraffes_elastic_net_beauty_err),
                        opacity = 1,
                        # marker=dict(color='red'),

                )
trace5 = go.Scatter(
                        x = list(df_random.num_images),
                        name = "Estimation Error using Linear Regression - Random K shared",
                        y = list(df_random.giraffes_linear_beauty_err),
                        opacity = 0.5,
                        # marker=dict(color='blue')

                )

trace6 = go.Scatter(
                        name = "Estimation Error using Elastic Net Regression - Random K shared",
                        x = list(df_random.num_images),
                        y = list(df_random.giraffes_elastic_net_beauty_err),
                        opacity = 1,
                        # marker=dict(color='red'),

                )

data = [trace1, trace2, trace3, trace4, trace5, trace6]
fig = dict(data=data, layout=layout)
iplot(fig)



    


















    
























In [21]:



    


layout= go.Layout(
                    showlegend=True,
                    legend=dict(
                        x=0.5,
                        y=1.25,
                        font=dict(size=15)
                    ),
                    xaxis= dict(
                        title= 'K (number of images each contributor shares)',
                        ticklen= 5,
                        zeroline= True,
                    ),
                    yaxis=dict(
                        ticklen= 5,
                        #range=range
                    )
                )
trace1 = go.Scatter(
                        x = list(figs.num_images),
                        name = "Estimation Error using Logistic Regression",
                        y = list(figs.zebras_ada_boost_beauty_err),
                        opacity = 0.5,
                        marker=dict(color='blue')

                )

trace2 = go.Scatter(
                        name = "Estimation Error using Ada Boost",
                        x = list(figs.num_images),
                        y = list(figs.zebras_ada_boost_non_zero_err),
                        opacity = 1,
                        marker=dict(color='red'),

                )

data = [trace1, trace2]
fig = dict(data=data, layout=layout)
iplot(fig)



    


















    






---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-21-f035f78b4159> in <module>()
     17                 )
     18 trace1 = go.Scatter(
---> 19                         x = list(figs.num_images),
     20                         name = "Estimation Error using Logistic Regression",
     21                         y = list(figs.zebras_ada_boost_beauty_err),

NameError: name 'figs' is not defined
















In [54]:



    


with open("../data/ggr_uuid_annot_uuid_map.json") as fl:
    j = json.load(fl)



    











In [55]:



    


j



    


















    
Out[55]:








{'4f95c9d7-6307-50d0-fe69-90928b4a19b3': ['10ae22d8-aed9-40f7-b665-04dfd3842ee7'],
 '09d61b8a-f592-02e7-f1e5-bb021948e1f5': ['cd6d2306-6d33-41bb-8c23-8fe5295fdeb5'],
 '6e074fbc-d78c-6cfd-3a09-13a7763aad56': [None],
 '96335179-03a6-02b2-0ffa-fb09a2bc2a1d': ['d339918e-06af-422e-98a4-cefe35dd1870',
  'ae7d33a0-af63-4140-a288-7e8311043795',
  'b3b11ac7-0051-4beb-9080-fa7a7246ff40',
  'e7256080-9d6f-4e45-9073-0c6c50f66b56'],
 'ca53b3ee-fd62-ab00-c02e-53036af885f1': ['19c6caba-0f9a-4c55-b8cf-0ca025ab33a7'],
 '71137829-9d7d-e51b-bc5b-b920d824de50': [None],
 '758557cb-2bc5-29c7-a004-39da3e193e6d': ['eae90ca7-bcc8-449f-9fe8-d6b53d562fe5'],
 '92e5c705-f01c-4177-4da0-6ad28475bf5e': ['a4c8f7db-3384-4649-bcea-1a55cb12ffa0'],
 '4693751d-f25e-81b6-5c98-8f55f7e3b64b': ['8bc719c4-1de7-4e57-9317-976166317de1'],
 '466334ff-31b0-d7e2-e861-34d4b6cf7083': ['e4ed796e-43b3-43d2-a63a-c03913ddc74e',
  '29a19518-c529-4cc5-9f66-9d056c47558e',
  'aa22e9b7-3e31-4f92-9201-d40ecc386e2b'],
 'b4f13f7f-1433-462a-2f3f-ae9257ab1cd5': ['3bd0bd6f-8eef-4188-a602-6ebfb188c738'],
 '3b8ca4f2-f823-e175-c40f-7362d2c26c60': ['8a6e011d-4656-49fe-b4ff-18337ef06fca'],
 '45bff3a7-68f4-85d0-3d71-d8e92e009ce4': ['49f6b50b-fbba-4c3a-94f8-89f5589731bb'],
 '44230eb4-3f0e-703d-f7e0-d2beaad31e86': ['5c1dca7c-64df-435a-8833-5eacf210017c',
  '724eea97-9439-4a12-b120-911b2154b923'],
 '8a150b3f-0161-e6fb-822a-8c825bf01969': [None],
 '6695cea9-b15e-8571-2cc2-1305c032301f': ['33cd739e-8b50-4dfa-a803-7e9b4692202c'],
 'c55ccbca-ea3a-4469-b6d4-bc5374493016': ['82dc39f2-ec80-4ec4-bcc4-f9bf654e5fe5',
  'c4df3b20-ade6-452f-95e8-01053443ba2b'],
 '4a7e6e0f-1567-8f73-459b-9a744abebc7a': ['7f011a24-ebb2-43b7-abbe-2f18aacd8e26',
  '16a2a22e-637c-48e9-847b-796d550e3eed',
  '14622067-ba45-47c6-a02e-2c6377709765'],
 '1dc94d26-35a1-ca41-60f5-f8f255f7f9f4': ['07eaaa73-ea77-4014-94a3-cbf66a9a8007'],
 '244c6a78-bac8-8fb9-b28f-6ef46b95c069': ['41ef38bb-bac8-46f4-8059-b14dcc15f6a2',
  '9582ed3b-c8c5-4a3d-b2ab-37a91c8a7e5c',
  'cf3804a8-1928-4135-8077-33a4934b1b08'],
 '7cc3d53b-3ec3-aea5-f8ac-9eb9ddff3a98': [None],
 '50581c1e-fbe1-ab4c-7322-f59aed478975': [None],
 'e670730c-8321-0e1b-0aca-f2edfdab27c6': ['85a40dd8-0212-4b37-a56c-2f1f29b870fc'],
 '7d1a4764-a9f1-8599-91c7-16571bfbfcfb': [None],
 '16a65831-778a-c924-b525-d4664b84bd6d': [None],
 '719ecc8e-f7ed-e2e4-79cf-1bf04703b6ba': ['7100d108-441e-4fc7-9522-153b012a109f'],
 '96740601-ab89-8d2a-af69-de4c6f21801b': [None],
 'a5905327-a628-312f-e3bc-84bfc0a47cb8': [None],
 'eead5b62-16a5-922c-b6e2-c375c24fa9ed': [None],
 '60ef2782-aa91-cefc-f8e3-44188f6654c3': [None],
 '99723a0f-4458-6260-1400-bde2345a3bb8': ['0a9ab2d4-6e65-4ba0-ac11-86671a6b58eb'],
 '6d7ab695-db2b-d4de-de91-28348d261276': ['3eddf2d0-d41d-4ff5-83f0-92039def71a4',
  '3dae0e9f-69fd-4694-9e14-dd692f8b709e'],
 '5bd416ca-b101-c77d-6b65-09bf664e9d2b': ['85a830f8-5528-4354-9af2-ac61f9814bc8'],
 '09ace80e-45c3-006a-0112-31f7157c7dea': [None],
 '63a98787-0842-c0fa-4962-8b669b0197c2': [None],
 'efb78c75-506a-c67c-ee75-a4d506ccfdf8': ['2fcfc2c5-67b9-4fe2-bf07-81f61263cb10',
  '5e7bb734-0731-4d8b-8c10-59cdf5553261'],
 '1d5e5e20-c8f9-6026-034d-3d3793c757b3': [None],
 'febd92a6-4fb0-aaaa-c158-0a53868238dc': [None],
 'e8281cb1-a8af-30d8-7b6a-582b313d9138': ['6231cc10-666b-4282-892c-ac7e71b4a254'],
 'f5663034-3265-d4e2-78dd-a897fbaf4749': [None],
 'b10b9934-dfda-2114-a305-7a87e19d7e94': [None],
 '70fe8fb8-4751-f72a-69fd-2589f36a50a6': [None],
 'f5ad9b96-f322-8bb0-e027-79217883154a': [None],
 'f0d65713-963a-a73e-6494-fd346a998797': ['78374a83-d295-489b-86ac-f977ae8e8525'],
 '072e14b6-6a15-adb5-e92d-1fc74911bf84': [None],
 '9b963a96-e1e0-3107-726e-84c64bbad282': ['d4315530-38e3-4947-ab9f-ab2bb4c89548'],
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  'a34a6eb2-71c4-4f53-ab26-c6a3d0776969'],
 'b1339151-0bfb-1d7f-a528-765830bedd75': [None],
 '314516e0-189e-d0f1-ca4a-7bc73358ee9a': ['4fc18de1-f647-4fa4-b572-f6af0d62aa99'],
 '86bdcba2-48b1-652b-2d3d-b1d81e0616a7': ['4c71fd86-6c67-4204-85b2-97307299fc31'],
 '86e2f049-f43f-1bb5-2818-4c543d74ad03': [None],
 '7a7160f1-ea81-c105-8ae2-3258bb6f0526': [None],
 'e07e433a-93bc-8732-3314-8d82792c729c': [None],
 '6a932794-bd68-4263-6952-bd7d52f62fed': ['f80a9d41-0b8c-45ae-8b06-f78cdc9c834e',
  '647c93a4-0260-42e0-830f-76398af59a4d'],
 '8e3018a3-d8e5-a1b3-b0b7-afc2ac86a8d2': ['54a9d0f5-d947-4097-ba8e-2f5535c6fd8d',
  '419d417e-ab1e-4dbb-a90b-fd95e18e342a'],
 '4ff50578-242e-abf2-917f-6ede8946adaa': ['22706e0a-87dd-49e8-9d74-68224de7b8cb'],
 '1ac6d431-e283-9b91-6be1-6b79112fa804': [None],
 '37e29385-3f68-d490-f82b-6dae39cd9654': ['0da8f4f0-82aa-49aa-8fcd-62ebae105b58'],
 'e042a5f4-d225-fa9a-ef97-e54df3c8fe5b': ['c6d3dd7b-8fd8-4b5c-b886-0b5abea12225'],
 '61bb065f-05f1-e44c-cbba-90c0134ce236': [None],
 '4f872f8c-77b4-da5b-e105-d9382f13fd6c': ['5e57989f-e799-45a3-aae9-d98b4f4434d2',
  '334cbd08-3951-4f4f-a6e4-ca44c2fa9f13'],
 '5b8355dc-27ed-963a-0189-a466b700fcf9': [None],
 ...}

















In [ ]:

Content source: smenon8/AnimalWildlifeEstimator

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