In [2]:

    

import matplotlib.pyplot as plt
import urllib
from bs4 import BeautifulSoup
from selenium import webdriver
import re
import os,sys, shutil
import time
from datetime import date
try:
    import cPickle as pickle
except:
    import pickle
import pprint
from collections import deque
from shutil import copyfile
import random
import glob
# Import the required modules
import cv2, os
import numpy as np
from PIL import Image
from sklearn.cross_validation import KFold
import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
# Logistic Regression
from sklearn import datasets
from sklearn import metrics


    

In [3]:

    

pkl_fl = open("linkedin_profiles.pickle","rb")
my_original_list=pickle.load(pkl_fl) # errors out here
pkl_fl.close()


    

In [3]:

    

my_original_list[0]


    

    
Out[3]:





{'Full_Name': 'Eryn Olson',
 'Gender': 'Female',
 'Profile_Url': 'https://media.licdn.com/mpr/mpr/shrinknp_400_400/AAEAAQAAAAAAAAbyAAAAJDMzZjQ4NjViLTIwZTctNDM0Yi05ODI5LTk0NmYzZWZmZDI5Yg.jpg',
 'Recommended_Ids': ['erynolson',
  'eryn-olson-62639510a',
  'eryn-olson-432679b1',
  'eryn-olson-1351aa74',
  'courtney-tillman-8b62a664',
  'chance-cozby-13235224',
  'bill-gates-b1a606b0',
  'sarah-eves-2937a719',
  'andrew-solheim-810a3517',
  'andrea-cundiff-76020535',
  'mollie-harper-6b850947',
  'tyler-shaw-934b7',
  'donna-conroy-66987340',
  'paul-wood-73455920'],
 'User_ID': 'eryn-olson-50328143',
 'age': 20}

In [26]:

    

directory = "Male"
    
if os.path.exists(directory):
    shutil.rmtree(directory)
    os.makedirs(directory)     
else:
    os.makedirs(directory) 

directory1 = "Female"

if os.path.exists(directory1):
    shutil.rmtree(directory1)
    os.makedirs(directory1)     
else:
    os.makedirs(directory1)     

directory2 = "Label_Images_Age"

if os.path.exists(directory2):
    shutil.rmtree(directory2)
    os.makedirs(directory2)     
else:
    os.makedirs(directory2)     
    
fileList = glob.glob("./Images/*.*")

for id,fp in enumerate(fileList):
    filename, file_extension = os.path.splitext(fp)
    uid = filename.split('/')[-1]
    #print fp
    for prof in my_original_list:
        if prof['User_ID'] == uid:
            prof_age = prof['age']
            
            if (0 <= prof_age <= 30):
                new_file_extension = 'Youth'
            else:
                new_file_extension = 'Senior'
            
            copyfile(filename + ".jpg", './Label_Images_Age/'+ uid + '.' + str(id) + "." + new_file_extension +'.jpg')


    

In [4]:

    

# For face detection we will use the Haar Cascade provided by OpenCV.
cascadePath = "haarcascade_frontalface_default.xml"
faceCascade = cv2.CascadeClassifier(cascadePath)

# For face recognition we will the the LBPH Face Recognizer 
recognizer = cv2.createLBPHFaceRecognizer()


    

In [5]:

    

def get_images_and_labels(path):
    # Append all the absolute image paths in a list image_paths
    
    image_paths = [os.path.join(path, f) for f in os.listdir(path)]
    # images will contains face images
    images = []
    # labels will contains the label that is assigned to the image
    labels = []
    #gender will contains 1 or 0 indecating male or female
    age =[]
    
    for image_path in image_paths:
        # Read the image and convert to grayscale
        try:
            image_pil = Image.open(image_path).convert('L')
            # Convert the image format into numpy array
            image = np.array(image_pil, 'uint8')
            # Get the label of the image
        except:
            pass
        
        nbr = int(os.path.split(image_path)[1].split(".")[1])
        age_current = os.path.split(image_path)[1].split(".")[2]
        #print nbr
        
        # Detect the face in the image
        faces = faceCascade.detectMultiScale(image)
        # If face is detected, append the face to images and the label to labels
        try:
            for (x, y, w, h) in faces:

                ref_image = image[y: y + h, x: x + w]
                resized = cv2.resize(ref_image, (100, 100), interpolation = cv2.INTER_AREA)
                edge_images = cv2.Canny(resized,100,200)
                
                images.append(np.hstack((resized.reshape(1,10000), edge_images.reshape(1,10000))))
                labels.append(nbr)

                if age_current == 'Youth':
                    age.append(0)
                else:
                    age.append(1)
                
                #face_file_name = "faces/face_" + str(y) + ".jpg"
                #cv2.imwrite(face_file_name, sub_face)
                
                cv2.imshow("Adding faces to traning set...", resized)
                cv2.waitKey(1)
        except:
            pass
    # return the images list and labels list
    #print "lables"
    #print labels
    #print "Age_current"
    #print age
    
    return images, labels, age


    

In [6]:

    

images, labels, ageList = get_images_and_labels('Label_Images_Age')

cv2.destroyAllWindows()


    

    




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731
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414
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594
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68
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1390
1966
1082
2
836
2452
675
2769
619
935
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3
665
1492
1995
559
969
1773
820
2155
126
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3256
1347
28
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2158
508
1762
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733
2666
1555
1545
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372
373
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791
1791
2853
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1977
388
1194
172
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1428
249
2184
1997
3040
513
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3141
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883
231
526
3146
1
775
875
819
273
2357
2153
2349
1851
380
759
1994
2779
2418
881
2933
96
3306
2413
2010
2672
2700
229
3150
3213
459
3068
955
201
952
1217
3392
491
1463
1199
363
2476
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920
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2339
2186
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175
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220
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496
29
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297
2178
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2398
1064
2041
3427
2635
1235
2392
1119
2197
398
1541
1353
2667
1489
300
164
3259
2844
2736
1974
1499
2314
865
2417
710
2840
1218
435
1523
1000
1592
1471
2874
2095
2038
2504
314
1436
1970
2631
218
3175
1504
3118
2379
3125
1548
61
971
2660
1318
2064
2100
204
3174
2430
729
553
1996
1011
3348
3038
2555
717
646
3070
1566
22
3060
2025
1071
3200
990
817
3377
1032
2686
423
948
684
383
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490
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615
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1832
991
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228
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1015
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276
2393
1277
1314
2554
2958
537
2569
1617
241
2334
1848
203
1109
1517
3436
2566
3312
307
780
1263
3000
1357
2252
2078
1663
1726
546
1648
807
263
1580
3022
275
1247
2086
1783
2716
778
748
3281
57
1308
216
2242
3034
1121
877
1770
381
2768
1643
2940
1935
2848
1251
1814
1610
2524
136
814
2976
908
2247
1379
788
2125
1621
1459
995
1384
2892
936
486
852
2679
801
2076
2675
1542
2832
1098
429
932
2886
2385
444
1415
907
502
70
1479
1244
3338
2386
436
631
1579
23
52
979
1853
3420
72
1153
5
335
358
2156
1644
3148
1737
2720
75
535
431
191
432
806
2851
1526
551
626
384
2914
1465
1797
3236
1177
2465
3165
1943
230
1238
3152
2553
550
288
1304
1507
1552
2814
3072
3123
2472
2420
2947
1141
3119
199
42
2245
889
712
1165
1781
1777
2397
3318
3260
474
1510
3231
2486
2582
1435
587
365
1926
3149
3329
520
1740
2750
465
1419
222
2135
1933
608
1572
2051
256
2866
3443
835
1550
2259
815
225
2714
2918
1188
1934
1468
1583
1598
379
3117
3369
2098
1968
2608
446
737
2932
270
1928
2475
1405
2312
259
150
600
640
1278
2463
3421
334
2451
127
1849
194
2130
1168
632
2868
1896
1720
154
139
2718
2233
1911
2689
845
1567
585
1547
340
522
3219
82
3191
453
2144
308
906
3292
404
2993
0
2696
2824
941
120
1137
3343
2996
1432
728
2364
2105
2330
3402
2924
3216
193
782
1365
1731
2089
1041
2419
939
3172
2006
3183
3120
1192
2229
3065
1918
1779
1924
2879
3042
1759
1370
2244
2974
1037
864
2621
1534
605
2938
1735
350
2670
2913
657
3394
1942
2912
574
1766
1316
3137
2804
485
207
982
1877
238
887
1443
1078
1025
2375
1481
1721
2021
1821
86
1404
3437
1729
3138
2571
900
3257
3140
695
487
347
558
533
967
155
857
2550
1237
1730
1409
2508
1352
1113
2796
3415
3277
1212
3327
2333
1101
3185
2433
258
323
330
1181
2175
2959
306
3109
2281
2511
2841
1009
329
1272
2306
2185
2336
1761
3324
1265
389
103
1389
3399
2008
2817
3154
1879
2448
2541
987
79
2347
309
2285
3425
3116
1057
774
3061
2916
998
2378
1038
901
2806
99
1952
1807
2857
784
1642
2342
3396
497
616
76
3245
190
1210
573
984
3157
1530
1040
1191
1219
1302
549
3208
3301
291
1307
2973
2823
2676
1335
2282
2110
1264
2356
1449
1615
2004
2484
3305
2221
813
1408
794
2023
688
911
2883
931
2934
773
2923
2538
2296
3382
691
1255
1039
2888
1007
2755
1490
1784
1260
2946
1588
1348
173
1201
2763
1170
1311
661
818
3347
2627
2441
1904
3079
1236
1306
2599
3353
3349
3229
772
71
3325
1117
2469
2223
2943
1227
1866
3218
1092
2536
1812
905
240
2323
336
2124
2272
2803
2987
839
1050
1252
1939
2318
2756
1288
195
2467
2258
656
1679
1535
1073
2338
2710
442
2559
2119
2563
1367
2455
1224
360
73
1824
2733
833
19
3355
1868
2651
2505
1243
1291
716
1573
3397
2509
1239
2818
556
1586
1927
87
458
1209
658
159
69
1920
895
1441
2967
3181
2096
425
324
2587
475
1095
697
1905
2706
1640
583
624
3121
2056
1589
1328
2123
3405
1270
1554
1081
2895
142
3434
1317
2018
1372
1562
2436
1245
1273
102
2945
1176
1225
2548
494
2901
506
1068
3128
3214
3411
1162
1626
338
2979
2054
1889
2294
313
3129
2552
2766
1850
2970
47
569
2371
855
244
945
1732
985
420
1636
885
743
2094
3361
2981
2039
1222
331
95
2040
989
642
1106
2906
2115
1984
1377
1519
1869
3100
1453
2547
1375
983
456
2146
170
1502
224
2416
1941
1019
1699
2179
1559
1332
2520
1656
1897
1650
2450
292
400
1042
1667
2434
1345
3026
1240
1166
221
2443
13
3094
2593
3145
2477
1150
2466
3322
1414
859
74
1842
247
197
2060
1451
2470
3084
223
1159
843
2044
1319
750
790
3240
682
2956
2063
2784
564
3310
3233
1775
2374
320
3264
1044
2059
2915
2896
3393
3328
2765
2070
1226
2842
3177
2922
1100
2637
576
3011
1959
362
1840
411
410
719
3254
2061
1503
3251
2951
2600
621
333
2741
2726
1515
3372
2941
1253
148
2011
40
3193
2899
1539
2919
2833
269
873
856
606
2503
1282
3215
108
3435
2468
2780
842
723
1157
757
2680
2549
2707
3179
613
3241
1595
3364
3227
1931
3290
3080
169
2495
2066
1330
112
3014
2963
1875
2069
1096
2080
3101
997
591
722
1894
185
1385
1482
1287
147
1967
3036
137
294
1776
2449
1855
874
1538
2162
1122
1725
2822
650
3423
699
3207
2176
428
578
2075
567
355
3408
3156
2310
1484
1140
3442
738
914
3300
2521
2351
3332
531
1629
1182
3387
986
2108
1712
1908
851
2820
1274
1949
192
3008
1639
1600
1079
1558
2648
1334
3076
2767
3378
568
48
2885
2298
2000
3346
2496
2454
1452
283
393
570
992
3153
1031
2643
2955
915
1292
1916
3135
649
3115
2173
27
55
1483
2299
702
509
1080
2207
3127
235
1655
528
1649
2849
870
278
370
2581
1413
1293
2159
1902
1581
2813
1973
1393
2634
3012
1298
683
1753
3071
415
1284
739
1543
3023
595
3263
2216
1021
1831
2957
1594
2782
681
762
2786
2944
1179
1913
745
3211
2997
1746
1280
1052
202
2939
2029
2622
1056
1682
744
3134
lables
[1023, 151, 2801, 1864, 1183, 1687, 260, 1922, 1745, 2045, 2411, 3274, 83, 1912, 1024, 1024, 2653, 2653, 2954, 3187, 2717, 938, 296, 296, 1131, 3063, 3403, 1525, 1458, 804, 516, 450, 1246, 2758, 3282, 2423, 3316, 1722, 1930, 2835, 2263, 2431, 2602, 433, 1790, 482, 482, 3250, 2910, 2354, 2325, 1094, 1094, 1094, 2446, 3027, 2128, 2128, 3016, 412, 1882, 1874, 2604, 709, 4, 2903, 2903, 3389, 3389, 1985, 3339, 345, 140, 2439, 1940, 277, 3424, 673, 1358, 1962, 3400, 3032, 2657, 2028, 1433, 1213, 1213, 1771, 2387, 2929, 2929, 2540, 3057, 2157, 2748, 844, 834, 2789, 2867, 2168, 2168, 2249, 541, 2067, 2067, 53, 53, 2137, 2526, 2526, 1574, 880, 2995, 1396, 2236, 2412, 1816, 2460, 704, 1953, 840, 3112, 953, 1756, 26, 328, 3308, 2269, 2545, 2545, 2545, 1806, 214, 663, 3370, 43, 43, 1400, 666, 2480, 2170, 1852, 2169, 1505, 2456, 2456, 2352, 364, 1929, 3053, 1144, 2880, 2880, 2290, 2516, 2984, 3017, 538, 676, 1516, 1724, 1439, 3056, 3056, 2724, 2724, 394, 1450, 3206, 1758, 1758, 1758, 680, 2251, 1744, 1827, 2590, 3252, 1028, 2727, 2609, 2030, 977, 3167, 1329, 838, 2646, 1014, 3058, 1964, 2049, 1123, 2227, 1061, 802, 3182, 796, 100, 100, 2406, 2406, 3285, 337, 1888, 254, 3205, 1969, 2109, 643, 659, 2691, 1609, 951, 705, 1906, 1906, 2264, 609, 274, 2875, 2875, 1611, 1846, 1846, 803, 1022, 2435, 413, 1455, 2287, 2053, 2191, 2191, 2253, 1631, 2615, 3099, 687, 1149, 670, 897, 1685, 2661, 2661, 130, 385, 385, 1207, 3365, 1670, 105, 1051, 1677, 1010, 2241, 1407, 418, 418, 783, 81, 81, 81, 671, 3158, 3158, 1283, 470, 2614, 1618, 2126, 652, 460, 396, 3143, 1564, 2935, 237, 1657, 1026, 1343, 1665, 926, 926, 1151, 2065, 2065, 3171, 1524, 59, 3278, 1271, 2704, 827, 827, 954, 954, 1733, 1858, 1858, 1858, 2649, 3108, 3330, 317, 644, 824, 1169, 3359, 1053, 3048, 30, 443, 443, 437, 1355, 3169, 1514, 2034, 3003, 1114, 1871, 1871, 1202, 1625, 1625, 149, 2596, 2355, 2847, 2705, 3267, 1411, 1142, 1152, 980, 980, 2542, 2542, 2583, 2777, 2250, 2893, 755, 755, 1461, 3111, 1016, 1765, 3391, 367, 1472, 1049, 2606, 2606, 1728, 686, 749, 1604, 1086, 2574, 957, 1597, 1789, 1825, 1825, 2340, 2058, 910, 2317, 589, 447, 2083, 2444, 1981, 863, 2749, 2402, 2633, 198, 2568, 2530, 2530, 2530, 2530, 886, 90, 792, 2369, 2369, 2369, 25, 295, 1366, 3133, 2042, 2042, 1739, 1546, 1546, 15, 847, 2122, 135, 1346, 3131, 2711, 2493, 2493, 1487, 1487, 1487, 2424, 2077, 925, 2732, 339, 339, 590, 590, 590, 1173, 1070, 2410, 994, 1549, 399, 399, 399, 399, 1310, 1310, 964, 1711, 949, 332, 2663, 893, 893, 1707, 1707, 2421, 2421, 2382, 3345, 3345, 1847, 854, 862, 268, 161, 1743, 610, 1354, 1354, 2703, 2703, 2703, 603, 603, 685, 2794, 2794, 1132, 1132, 1857, 2494, 1205, 402, 2860, 2860, 2860, 1719, 2237, 152, 113, 3246, 693, 693, 1992, 1537, 1309, 1085, 200, 2395, 3356, 3262, 2802, 2219, 2783, 1576, 2082, 2082, 1204, 2998, 2409, 2409, 167, 179, 2489, 2489, 1469, 3284, 1710, 1710, 1529, 930, 942, 2335, 1180, 1960, 1299, 1496, 2292, 1403, 2662, 1570, 1570, 104, 104, 1420, 1634, 832, 2745, 2200, 1561, 2161, 2161, 310, 2846, 2210, 6, 1077, 466, 3114, 3114, 561, 561, 1315, 1785, 89, 1815, 1815, 1815, 581, 1999, 2713, 484, 234, 1127, 1127, 758, 758, 3268, 3268, 1003, 3224, 3224, 3224, 3224, 3224, 884, 469, 1475, 1475, 1475, 3001, 2273, 2284, 118, 3253, 3253, 1020, 1020, 785, 2377, 77, 287, 539, 808, 305, 2999, 2683, 1364, 730, 188, 1256, 2628, 1983, 1983, 2514, 850, 1801, 2898, 3243, 3243, 924, 593, 369, 349, 1378, 32, 1196, 1196, 1961, 741, 741, 1046, 1046, 3326, 2525, 2525, 1540, 1540, 1540, 2872, 2872, 3180, 232, 3110, 3039, 1399, 1399, 597, 597, 1988, 1681, 2368, 312, 141, 552, 301, 391, 3390, 2427, 672, 1395, 3081, 2688, 1755, 1755, 734, 734, 3309, 937, 694, 3357, 64, 2429, 60, 3087, 1296, 1296, 1296, 3228, 1958, 1958, 1885, 1885, 3122, 2488, 357, 2329, 2949, 2551, 1178, 311, 311, 2891, 2009, 3321, 3005, 3005, 1154, 3430, 2367, 2367, 2367, 1104, 3414, 3414, 1520, 1520, 2171, 189, 2930, 3007, 483, 965, 3404, 1748, 946, 946, 1963, 879, 2928, 3210, 2971, 1688, 1915, 386, 1862, 828, 641, 280, 3220, 3002, 3088, 2985, 2501, 2149, 2091, 265, 265, 2471, 80, 2638, 2638, 575, 2788, 1607, 299, 3151, 2320, 2320, 2320, 3166, 3103, 3103, 3223, 3223, 882, 2350, 2238, 2311, 3188, 2391, 2391, 2391, 2391, 501, 2858, 488, 1782, 2980, 795, 1214, 78, 1606, 1606, 1233, 3383, 3383, 789, 789, 2572, 1006, 1062, 547, 17, 1232, 2953, 1805, 3334, 3028, 1431, 579, 2616, 1584, 3019, 1440, 1808, 1808, 1418, 1937, 1423, 1129, 2199, 2097, 2747, 1380, 2630, 1654, 2665, 1394, 3192, 1494, 2843, 1799, 2839, 2474, 2474, 799, 1466, 1466, 1466, 1018, 1982, 1982, 1982, 611, 1500, 830, 298, 2268, 2373, 1704, 2027, 1339, 2838, 2838, 409, 3385, 3439, 700, 963, 1803, 1678, 1120, 1120, 1147, 1147, 2900, 88, 2632, 2632, 1686, 2522, 2522, 2267, 1406, 1406, 771, 2363, 940, 2805, 2831, 1872, 2902, 2902, 2925, 3304, 970, 210, 210, 2326, 2326, 1792, 1792, 1792, 878, 1146, 3045, 918, 1261, 1261, 226, 1488, 2882, 2882, 2882, 973, 1269, 2968, 2327, 14, 972, 837, 837, 655, 950, 1713, 1074, 1624, 1624, 1155, 1155, 2414, 2133, 628, 1794, 439, 2020, 1387, 2659, 2482, 2482, 2151, 2348, 2348, 2734, 2702, 1063, 2136, 1909, 3142, 3142, 3142, 1478, 271, 3030, 2917, 2810, 2586, 2624, 2624, 633, 633, 1769, 1544, 1605, 725, 3212, 3212, 2591, 3098, 101, 3232, 325, 1612, 1612, 205, 205, 1630, 1630, 2081, 1090, 124, 614, 178, 3271, 489, 1793, 1793, 2461, 2775, 714, 479, 479, 2188, 2982, 448, 448, 1780, 2166, 2613, 2613, 853, 929, 196, 196, 1331, 1045, 2160, 707, 1341, 2277, 2407, 2652, 1381, 2507, 3194, 2712, 2712, 3044, 770, 770, 1684, 1979, 1980, 3091, 960, 960, 2361, 2629, 3433, 1837, 735, 2762, 3269, 1658, 1658, 507, 1186, 504, 2102, 2228, 620, 2498, 1336, 3074, 3074, 718, 530, 449, 449, 85, 944, 1200, 1787, 2322, 2322, 3033, 3303, 3438, 3438, 1467, 898, 3203, 251, 1641, 227, 2812, 1593, 3363, 3363, 2864, 3367, 3367, 3367, 3297, 2093, 2033, 2362, 3159, 377, 377, 2792, 2792, 1772, 452, 1289, 781, 2729, 2729, 2729, 2798, 2798, 2239, 2965, 184, 1521, 3248, 3302, 1948, 1666, 3062, 662, 2519, 1585, 1036, 2265, 2265, 2265, 956, 416, 927, 1843, 1843, 2491, 2491, 1506, 2576, 1860, 2534, 3270, 145, 1084, 521, 1513, 1513, 2743, 1457, 1577, 1998, 1445, 3422, 2681, 2681, 1865, 1865, 3406, 2107, 1671, 511, 2776, 2057, 2057, 2057, 1422, 1422, 1947, 1947, 290, 290, 3024, 1991, 753, 1321, 3289, 3289, 3289, 2532, 2026, 390, 848, 959, 2255, 860, 2222, 2578, 2658, 3217, 3217, 2790, 1881, 1881, 1881, 1881, 1881, 1881, 1845, 2271, 2598, 2598, 110, 110, 2746, 2256, 457, 98, 98, 98, 1103, 1350, 2962, 2001, 1697, 441, 899, 2845, 3035, 3418, 2856, 2856, 2319, 2319, 2612, 1669, 2654, 1870, 571, 1172, 1172, 1802, 2154, 1425, 1474, 1474, 1474, 1473, 2481, 2147, 1778, 293, 904, 2850, 2224, 1295, 524, 3320, 3320, 424, 503, 1599, 2641, 2855, 2855, 667, 667, 667, 667, 3279, 3279, 1919, 18, 1486, 1486, 272, 2206, 451, 1861, 1861, 698, 698, 2492, 2492, 346, 1241, 2862, 677, 187, 187, 180, 3189, 2190, 1373, 58, 943, 1646, 1646, 3095, 1161, 2869, 3160, 33, 1819, 1884, 1884, 580, 826, 826, 826, 1553, 1553, 1091, 1198, 2770, 653, 1938, 3429, 3429, 3429, 1211, 2640, 2640, 2908, 2908, 1189, 2487, 708, 1527, 975, 2346, 3234, 2201, 1532, 3147, 751, 751, 342, 2671, 2671, 2671, 622, 1386, 2005, 3358, 1518, 2403, 3298, 3298, 3298, 2601, 2438, 3417, 3258, 3258, 2116, 3046, 3046, 2645, 2926, 1972, 1017, 1248, 3190, 2211, 2211, 1898, 1823, 1823, 1810, 166, 2307, 1434, 2483, 2483, 1754, 1754, 3004, 1917, 1672, 746, 3021, 732, 1613, 1187, 253, 805, 2209, 2773, 2772, 3242, 2664, 2760, 2760, 2837, 1266, 1703, 849, 625, 3344, 1702, 2694, 2694, 39, 1863, 162, 1160, 2799, 91, 91, 3006, 505, 2243, 2243, 2884, 1901, 1901, 421, 2376, 3077, 478, 1603, 1603, 284, 35, 419, 1221, 1221, 267, 267, 2134, 1290, 2231, 3351, 2610, 3050, 1327, 1327, 3239, 1727, 1501, 1501, 557, 1259, 438, 2141, 2090, 3440, 2687, 654, 2215, 2163, 1216, 1841, 1698, 2819, 1661, 2072, 2072, 1701, 468, 933, 2024, 2024, 1033, 2715, 3360, 366, 2257, 2150, 1575, 2360, 996, 133, 523, 1464, 2564, 422, 2270, 2270, 2744, 2535, 1891, 2617, 1651, 2143, 2396, 2035, 519, 352, 1281, 1921, 3168, 2544, 2195, 1124, 1124, 1058, 1058, 215, 1371, 1130, 3275, 3078, 245, 674, 2722, 3064, 2302, 2889, 3069, 2071, 902, 1878, 2017, 1887, 1627, 1627, 3202, 1867, 2623, 392, 2605, 1087, 31, 31, 2952, 2518, 2112, 2937, 923, 3178, 577, 2401, 2401, 106, 2735, 2735, 793, 793, 351, 2816, 2816, 2225, 1946, 177, 572, 572, 498, 3428, 1623, 1623, 1856, 1856, 1401, 1976, 1229, 3176, 3162, 134, 981, 3441, 1320, 2742, 279, 3426, 2695, 2515, 2873, 16, 1097, 1633, 2881, 1223, 2297, 440, 1950, 1715, 756, 756, 2226, 2226, 111, 1030, 3299, 3307, 163, 163, 1338, 1338, 1568, 2283, 1716, 1945, 3265, 1714, 1004, 2669, 405, 326, 2960, 2990, 869, 2865, 1951, 1054, 696, 696, 696, 2510, 2510, 2218, 2699, 3395, 2120, 2120, 2138, 2138, 2138, 1145, 1990, 1652, 97, 3106, 703, 2585, 1143, 1143, 2447, 1708, 2240, 1820, 1809, 731, 492, 2370, 2370, 2370, 3362, 1511, 2558, 414, 809, 1230, 1230, 3413, 2592, 2328, 2328, 2725, 2964, 1089, 1986, 165, 2584, 2969, 2528, 1368, 635, 219, 1676, 841, 1628, 1495, 1485, 3416, 2920, 2180, 1013, 3124, 594, 1268, 68, 2103, 2639, 1390, 1966, 1082, 2, 836, 2452, 675, 2769, 619, 935, 645, 3, 3, 665, 1492, 1995, 1995, 559, 969, 969, 1773, 820, 2155, 126, 3198, 3198, 3256, 1347, 28, 28, 1907, 2321, 1034, 2087, 2036, 2036, 2158, 2158, 508, 1762, 1125, 2948, 2506, 2506, 3432, 733, 2666, 1555, 1545, 1545, 2118, 2118, 372, 373, 1427, 2479, 2343, 1250, 1250, 1250, 791, 791, 791, 791, 1791, 2853, 2177, 2358, 1977, 388, 1194, 172, 1695, 2808, 2808, 1428, 249, 2184, 1997, 3040, 513, 3384, 3209, 3141, 962, 2279, 3354, 3354, 1637, 517, 1587, 3379, 2183, 2308, 2073, 2994, 2994, 2589, 2589, 1356, 1462, 1305, 1305, 1108, 1108, 1361, 798, 798, 1774, 1774, 883, 231, 526, 3146, 1, 775, 875, 819, 273, 2357, 2153, 2153, 2153, 2153, 2349, 2349, 1851, 380, 759, 1994, 2779, 2418, 881, 2933, 2933, 2933, 96, 3306, 2413, 2010, 2672, 2700, 229, 3150, 3213, 3213, 459, 3068, 955, 201, 952, 1217, 1217, 3392, 491, 1463, 1463, 1199, 363, 363, 2476, 2476, 2966, 555, 920, 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Age_current
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In [7]:

    

with open("tmp_age.pickle", "wb") as f:
    pickle.dump((images, labels, ageList), f)


    

In [ ]:

    

with open("tmp_age.pickle", "rb") as f:
    images, labels, ageList = pickle.load(f)


    

In [11]:

    

images[0].shape


    

    
Out[11]:





(1, 20000)

In [9]:

    

res_images = []
res_age = []

# for img in images:
#     res_images.append(img.reshape(1,10000))    # (100*100 image size)

for age in ageList:
    res_age.append(np.array(age).reshape(1,))
    
res_images = np.concatenate(images, axis=0)
res_age = np.concatenate(res_age,axis=0)


    

In [ ]:

    




    

In [10]:

    

X,y = res_images,res_age


    

In [11]:

    

X.shape


    

    
Out[11]:





(2968, 20000)

In [12]:

    

y.shape


    

    
Out[12]:





(2968,)

In [ ]:

    




    

In [13]:

    

def predict(X,y):

    kf = KFold(X.shape[0], n_folds=10, shuffle=True)
    accuracy = 0.0
    precision = [0, 0]
    recall = [0, 0]
    f_measure = [0, 0]

    lr = LogisticRegression()
    inbuilt_accuracy_score = 0.0

    for test, train in kf:
        X_Train, X_Test = X[train], X[test]
        Y_Train, Y_Test = y[train], y[test]

        lr.fit(X_Train, Y_Train)

        pred = lr.predict(X_Test)

        inbuilt_accuracy_score += accuracy_score(Y_Test, (pred))

    inbuilt_accuracy_score /= 10


    print "\nAccuracy from inbuilt Method: ", inbuilt_accuracy_score
    print(metrics.classification_report(Y_Test, pred))
    print(metrics.confusion_matrix(Y_Test, pred))


    

In [14]:

    

predict(X,y)


    

    




Accuracy from inbuilt Method:  0.568432761956
             precision    recall  f1-score   support

          0       0.66      0.63      0.64      1703
          1       0.39      0.42      0.41       969

avg / total       0.56      0.55      0.56      2672

[[1067  636]
 [ 561  408]]

In [16]:

    

X_without_edge,y = res_images[:,:10000],res_age
X_without_edge.shape


    

    
Out[16]:





(2968, 10000)

In [17]:

    

predict(X_without_edge,y)


    

    




Accuracy from inbuilt Method:  0.578615779486
             precision    recall  f1-score   support

          0       0.65      0.73      0.69      1685
          1       0.42      0.34      0.38       987

avg / total       0.57      0.58      0.57      2672

[[1226  459]
 [ 652  335]]

In [ ]:

    




    

In [ ]:

    




    

In [23]:

    

#!/usr/bin/env python
import numpy as np
import matplotlib.mlab as mlab
import matplotlib.pyplot as plt
%matplotlib inline

#mu, sigma = 100, 15
#x = mu + sigma*np.random.randn(10000)
ages = [prof['age'] for prof in my_original_list]
x= np.array(ages) 
# the histogram of the data
n, bins, patches = plt.hist(x, 50, normed=1, facecolor='blue', alpha=0.75)

# add a 'best fit' line
#y = mlab.normpdf(bins, mu, sigma)
#l = plt.plot(bins, y, 'r--', linewidth=1)

plt.xlabel('Age')
plt.ylabel('Probability')
plt.title(r'$Histogram\ \ of\ \ Ages$')
plt.axis([1, 80, 0, 0.09])
plt.grid(True)

plt.show()