In [138]:
# takes in a joined dataframe by tuples and generates a matrix by mapping the rows to feature vectors
DATA_PATH = "../joined_unfeaturized.csv"
import pandas as pd
df_all = pd.read_csv(DATA_PATH, na_values=['-'])
df_all = df_all.where((pd.notnull(df_all)), None)
df_all.head(5)
df_all.head(15)
Out[138]:
feature-311
tuple
911-reports
0
[{'Category': 'Sign Repair', 'TimeBin': 627414...
(6274149.810962193, -122.42479733333332, 37.77...
[]
1
[{'Category': '311 External Request', 'TimeBin...
(68448950.51830366, -122.45177022222221, 37.76...
[]
2
[{'Category': 'Litter Receptacles', 'TimeBin':...
(180695514.55571115, -122.50571599999999, 37.7...
[]
3
[{'Category': 'Rec and Park Requests', 'TimeBi...
(198060742.09701943, -122.50571599999999, 37.7...
[]
4
[{'Category': 'General Requests', 'TimeBin': 1...
(166811428.19983998, -122.42479733333332, 37.7...
[]
5
[{'Category': 'General Requests', 'TimeBin': 1...
(125240125.90398079, -122.49222955555555, 37.7...
[]
6
[{'Category': 'Street and Sidewalk Cleaning', ...
(164827987.29185838, -122.41131088888888, 37.7...
[]
7
[{'Category': 'Street and Sidewalk Cleaning', ...
(101115007.92118424, -122.42479733333332, 37.7...
[]
8
[{'Category': 'Tree Maintenance', 'TimeBin': 8...
(80592466.28145629, -122.43828377777778, 37.78...
[]
9
[{'Category': 'General Requests', 'TimeBin': 1...
(168268650.0914183, -122.43828377777778, 37.77...
[{'Category': 'OTHER OFFENSES', 'TimeBin': 168...
10
[{'Category': 'Sidewalk or Curb', 'TimeBin': 1...
(16110397.579115825, -122.43828377777778, 37.7...
[]
11
[{'Category': 'Graffiti Public Property', 'Tim...
(194822471.22684535, -122.42479733333332, 37.7...
[]
12
[{'Category': 'Damaged Property', 'TimeBin': 1...
(166163774.02580518, -122.41131088888888, 37.7...
[]
13
[{'Category': 'Tree Maintenance', 'TimeBin': 1...
(135157330.44388878, -122.46525666666668, 37.7...
[{'Category': 'LARCENY/THEFT', 'TimeBin': 1351...
14
[{'Category': 'Damaged Property', 'TimeBin': 1...
(115444356.52170436, -122.39782444444445, 37.7...
[]
In [102]:
df = df_all[0:11000]
df.head(2)
Out[102]:
feature-311
tuple
911-reports
0
[{'Category': 'Sign Repair', 'TimeBin': 627414...
(6274149.810962193, -122.42479733333332, 37.77...
[]
1
[{'Category': '311 External Request', 'TimeBin...
(68448950.51830366, -122.45177022222221, 37.76...
[]
In [103]:
# remove any rows that have problematic data
i = []
index = -1
for row_tuple in df.iterrows():
row = row_tuple[1] # a pandas Series
#print row
index += 1
if (index > 500000):
break
try:
f3 = eval(row['feature-311'])
except NameError as e:
i.append(index)
continue
f9 = eval(row['911-reports'])
print i
df = df.drop(df.index[i])
[2, 9, 26, 49, 87, 101, 108, 109, 117, 126, 146, 170, 180, 184, 187, 195, 217, 235, 249, 251, 287, 316, 334, 340, 353, 370, 379, 401, 421, 445, 448, 472, 473, 492, 496, 507, 523, 531, 532, 537, 538, 557, 575, 586, 588, 601, 612, 657, 705, 706, 711, 748, 752, 787, 815, 824, 825, 828, 877, 892, 945, 957, 974, 1003, 1012, 1024, 1035, 1054, 1089, 1094, 1152, 1157, 1176, 1179, 1249, 1266, 1274, 1324, 1327, 1358, 1365, 1368, 1379, 1386, 1403, 1413, 1420, 1469, 1472, 1503, 1507, 1510, 1513, 1522, 1531, 1541, 1594, 1618, 1619, 1628, 1654, 1669, 1689, 1707, 1717, 1719, 1721, 1728, 1733, 1749, 1758, 1763, 1798, 1802, 1805, 1806, 1824, 1836, 1842, 1864, 1877, 1925, 1938, 1950, 2012, 2017, 2018, 2043, 2044, 2050, 2084, 2090, 2094, 2112, 2116, 2142, 2168, 2208, 2248, 2265, 2266, 2297, 2309, 2347, 2399, 2404, 2407, 2424, 2457, 2467, 2472, 2484, 2487, 2490, 2502, 2504, 2556, 2571, 2600, 2619, 2646, 2661, 2668, 2674, 2719, 2735, 2740, 2749, 2756, 2764, 2770, 2772, 2778, 2808, 2820, 2822, 2828, 2924, 2963, 2970, 2975, 2985, 2992, 2997, 3032, 3035, 3047, 3070, 3083, 3101, 3172, 3174, 3188, 3202, 3210, 3216, 3217, 3222, 3226, 3235, 3271, 3274, 3285, 3293, 3315, 3322, 3324, 3336, 3342, 3362, 3381, 3389, 3398, 3404, 3417, 3423, 3428, 3429, 3440, 3498, 3527, 3565, 3568, 3569, 3572, 3576, 3581, 3582, 3616, 3621, 3625, 3646, 3660, 3667, 3670, 3682, 3691, 3693, 3698, 3708, 3720, 3725, 3733, 3741, 3744, 3762, 3771, 3787, 3789, 3809, 3814, 3817, 3819, 3820, 3827, 3830, 3857, 3879, 3891, 3900, 3912, 3929, 3942, 3948, 3967, 3987, 3991, 4026, 4061, 4101, 4130, 4131, 4136, 4139, 4141, 4145, 4169, 4185, 4191, 4214, 4242, 4265, 4284, 4289, 4294, 4321, 4344, 4363, 4379, 4380, 4385, 4404, 4410, 4432, 4444, 4449, 4452, 4531, 4572, 4576, 4580, 4587, 4598, 4616, 4632, 4690, 4725, 4735, 4762, 4767, 4772, 4788, 4789, 4790, 4801, 4813, 4814, 4829, 4832, 4839, 4854, 4861, 4863, 4876, 4895, 4915, 4943, 4957, 4976, 4978, 4984, 5002, 5004, 5060, 5069, 5072, 5073, 5084, 5118, 5128, 5157, 5177, 5184, 5200, 5204, 5209, 5231, 5252, 5257, 5263, 5269, 5293, 5294, 5296, 5306, 5312, 5317, 5322, 5344, 5351, 5375, 5386, 5390, 5411, 5422, 5425, 5441, 5448, 5466, 5484, 5512, 5519, 5533, 5549, 5567, 5575, 5611, 5614, 5621, 5627, 5628, 5637, 5651, 5666, 5675, 5709, 5711, 5729, 5745, 5763, 5768, 5781, 5791, 5801, 5817, 5821, 5866, 5880, 5882, 5905, 5907, 5909, 5919, 5932, 5941, 5944, 5968, 5973, 5982, 5984, 5986, 6022, 6042, 6048, 6088, 6094, 6099, 6112, 6195, 6208, 6215, 6217, 6236, 6237, 6249, 6264, 6265, 6287, 6345, 6364, 6366, 6374, 6380, 6383, 6406, 6413, 6429, 6484, 6500, 6509, 6523, 6527, 6539, 6555, 6566, 6571, 6618, 6635, 6642, 6654, 6668, 6669, 6682, 6688, 6692, 6696, 6737, 6752, 6755, 6763, 6790, 6801, 6862, 6892, 6913, 6916, 6931, 6938, 6940, 6942, 6961, 6968, 6981, 7012, 7038, 7042, 7075, 7089, 7143, 7150, 7156, 7158, 7180, 7196, 7212, 7226, 7232, 7245, 7248, 7257, 7260, 7271, 7273, 7290, 7294, 7295, 7304, 7328, 7342, 7383, 7386, 7395, 7401, 7408, 7419, 7440, 7466, 7477, 7507, 7538, 7540, 7553, 7557, 7605, 7637, 7669, 7685, 7709, 7751, 7753, 7755, 7761, 7763, 7773, 7780, 7798, 7800, 7802, 7808, 7811, 7817, 7830, 7856, 7857, 7877, 7895, 7916, 7929, 7981, 7991, 8001, 8022, 8038, 8041, 8065, 8066, 8074, 8079, 8094, 8095, 8108, 8131, 8140, 8151, 8153, 8160, 8171, 8221, 8234, 8240, 8245, 8279, 8294, 8338, 8351, 8373, 8376, 8395, 8437, 8438, 8446, 8457, 8465, 8471, 8474, 8476, 8479, 8500, 8518, 8528, 8532, 8602, 8609, 8610, 8624, 8636, 8659, 8662, 8681, 8700, 8705, 8728, 8730, 8738, 8747, 8772, 8773, 8798, 8803, 8819, 8872, 8879, 8886, 8906, 8909, 8912, 8981, 8996, 9007, 9020, 9048, 9062, 9087, 9089, 9105, 9162, 9186, 9218, 9220, 9232, 9258, 9265, 9271, 9277, 9279, 9304, 9348, 9349, 9350, 9354, 9361, 9376, 9377, 9387, 9389, 9394, 9397, 9402, 9408, 9422, 9439, 9459, 9476, 9488, 9493, 9495, 9512, 9539, 9551, 9563, 9578, 9602, 9612, 9628, 9630, 9638, 9657, 9690, 9732, 9742, 9752, 9769, 9770, 9785, 9792, 9803, 9814, 9834, 9858, 9915, 9917, 9936, 9939, 9949, 9953, 9962, 9982, 9992, 10018, 10030, 10033, 10038, 10040, 10042, 10061, 10076, 10099, 10103, 10109, 10126, 10135, 10143, 10181, 10204, 10219, 10237, 10262, 10267, 10273, 10282, 10285, 10300, 10304, 10312, 10323, 10363, 10371, 10378, 10385, 10398, 10399, 10409, 10420, 10430, 10443, 10450, 10455, 10460, 10464, 10467, 10480, 10550, 10555, 10563, 10571, 10619, 10621, 10628, 10634, 10669, 10681, 10708, 10712, 10713, 10721, 10735, 10761, 10785, 10802, 10804, 10807, 10824, 10827, 10836, 10856, 10868, 10888, 10891, 10918, 10920, 10921, 10932, 10950, 10955, 10959, 10986, 10987, 10990, 10992, 10995]
In [105]:
#map the feature-311 data to a vector
df = df[0:10000]
df.head(50)
Out[105]:
feature-311
tuple
911-reports
0
[{'Category': 'Sign Repair', 'TimeBin': 627414...
(6274149.810962193, -122.42479733333332, 37.77...
[]
1
[{'Category': '311 External Request', 'TimeBin...
(68448950.51830366, -122.45177022222221, 37.76...
[]
3
[{'Category': 'Rec and Park Requests', 'TimeBi...
(198060742.09701943, -122.50571599999999, 37.7...
[]
4
[{'Category': 'General Requests', 'TimeBin': 1...
(166811428.19983998, -122.42479733333332, 37.7...
[]
5
[{'Category': 'General Requests', 'TimeBin': 1...
(125240125.90398079, -122.49222955555555, 37.7...
[]
6
[{'Category': 'Street and Sidewalk Cleaning', ...
(164827987.29185838, -122.41131088888888, 37.7...
[]
7
[{'Category': 'Street and Sidewalk Cleaning', ...
(101115007.92118424, -122.42479733333332, 37.7...
[]
8
[{'Category': 'Tree Maintenance', 'TimeBin': 8...
(80592466.28145629, -122.43828377777778, 37.78...
[]
10
[{'Category': 'Sidewalk or Curb', 'TimeBin': 1...
(16110397.579115825, -122.43828377777778, 37.7...
[]
11
[{'Category': 'Graffiti Public Property', 'Tim...
(194822471.22684535, -122.42479733333332, 37.7...
[]
12
[{'Category': 'Damaged Property', 'TimeBin': 1...
(166163774.02580518, -122.41131088888888, 37.7...
[]
13
[{'Category': 'Tree Maintenance', 'TimeBin': 1...
(135157330.44388878, -122.46525666666668, 37.7...
[{'Category': 'LARCENY/THEFT', 'TimeBin': 1351...
14
[{'Category': 'Damaged Property', 'TimeBin': 1...
(115444356.52170436, -122.39782444444445, 37.7...
[]
15
[{'Category': 'Street and Sidewalk Cleaning', ...
(30965965.19603921, -122.50571599999999, 37.76...
[]
16
[{'Category': 'Residential Building Request', ...
(108967814.78135628, -122.47874311111113, 37.7...
[]
17
[{'Category': 'Street and Sidewalk Cleaning', ...
(94355117.47969596, -122.47874311111113, 37.74...
[]
18
[{'Category': 'Sewer Issues', 'TimeBin': 18065...
(180655036.16983402, -122.50571599999999, 37.7...
[]
19
[{'Category': 'Sign Repair', 'TimeBin': 850450...
(85045088.7279456, -122.45177022222221, 37.762...
[]
20
[{'Category': 'General Requests', 'TimeBin': 1...
(170454482.92878577, -122.45177022222221, 37.7...
[]
21
[{'Category': 'Rec and Park Requests', 'TimeBi...
(61972408.77795561, -122.43828377777778, 37.78...
[]
22
[{'Category': 'SFHA Requests', 'TimeBin': 4897...
(4897884.691138228, -122.39782444444445, 37.74...
[]
23
[{'Category': 'Street and Sidewalk Cleaning', ...
(36592460.8329666, -122.45177022222221, 37.762...
[]
24
[{'Category': 'Sewer Issues', 'TimeBin': 15636...
(156368004.64352873, -122.45177022222221, 37.7...
[]
25
[{'Category': 'Graffiti Public Property', 'Tim...
(195267733.47149432, -122.50571599999999, 37.7...
[]
27
[{'Category': 'Sign Repair', 'TimeBin': 632677...
(63267717.12602521, -122.45177022222221, 37.76...
[]
28
[{'Category': 'Graffiti Private Property', 'Ti...
(14653175.687537508, -122.45177022222221, 37.7...
[]
29
[{'Category': 'Sewer Issues', 'TimeBin': 13965...
(13965043.127625527, -122.43828377777778, 37.7...
[]
30
[{'Category': 'Graffiti Private Property', 'Ti...
(143779226.63572717, -122.41131088888888, 37.7...
[]
31
[{'Category': 'Sidewalk or Curb', 'TimeBin': 1...
(161508759.64993, -122.43828377777778, 37.7312...
[]
32
[{'Category': 'General Requests', 'TimeBin': 6...
(66020247.36567314, -122.45177022222221, 37.73...
[]
33
[{'Category': 'Street and Sidewalk Cleaning', ...
(201744275.21184242, -122.39782444444445, 37.7...
[]
34
[{'Category': 'Sidewalk or Curb', 'TimeBin': 1...
(184095698.9693939, -122.43828377777778, 37.79...
[]
35
[{'Category': 'Graffiti Public Property', 'Tim...
(13762651.198239647, -122.46525666666668, 37.7...
[]
36
[{'Category': 'SFHA Requests', 'TimeBin': 1687...
(16879486.91078216, -122.43828377777778, 37.77...
[{'Category': 'OTHER OFFENSES', 'TimeBin': 168...
37
[{'Category': '311 External Request', 'TimeBin...
(79175722.77575515, -122.47874311111113, 37.77...
[]
38
[{'Category': 'Street and Sidewalk Cleaning', ...
(68044166.65953189, -122.43828377777778, 37.75...
[]
39
[{'Category': 'Graffiti Private Property', 'Ti...
(176364327.26685342, -122.42479733333332, 37.7...
[]
40
[{'Category': 'General Requests', 'TimeBin': 8...
(84437912.93978797, -122.50571599999999, 37.77...
[]
41
[{'Category': 'Street and Sidewalk Cleaning', ...
(69987129.18163633, -122.46525666666668, 37.73...
[{'Category': 'LARCENY/THEFT', 'TimeBin': 6998...
42
[{'Category': 'SFHA Requests', 'TimeBin': 1707...
(170737831.629926, -122.45177022222221, 37.782...
[]
43
[{'Category': 'Graffiti Private Property', 'Ti...
(161751629.96519306, -122.39782444444445, 37.7...
[]
44
[{'Category': 'Street and Sidewalk Cleaning', ...
(15665135.334466895, -122.42479733333332, 37.7...
[]
45
[{'Category': 'Damaged Property', 'TimeBin': 1...
(120747025.07161434, -122.47874311111113, 37.7...
[]
46
[{'Category': 'Sign Repair', 'TimeBin': 851260...
(85126045.49969995, -122.45177022222221, 37.79...
[]
47
[{'Category': 'Street Defects', 'TimeBin': 140...
(140743347.694939, -122.41131088888888, 37.731...
[]
48
[{'Category': 'SFHA Requests', 'TimeBin': 3444...
(34447106.3814763, -122.42479733333332, 37.710...
[]
50
[{'Category': 'Litter Receptacles', 'TimeBin':...
(101965054.02460492, -122.42479733333332, 37.7...
[]
51
[{'Category': 'General Requests', 'TimeBin': 1...
(167661474.30326065, -122.47874311111113, 37.7...
[]
52
[{'Category': 'SFHA Requests', 'TimeBin': 7553...
(75532668.04680938, -122.43828377777778, 37.75...
[]
53
[{'Category': 'Street and Sidewalk Cleaning', ...
(73387313.59531908, -122.47874311111113, 37.71...
[]
In [120]:
print df.iloc[11][0]
print df.iloc[11][2]
[{'Category': 'Tree Maintenance', 'TimeBin': 135157330.44388878, 'Request Type': 'Trees - Landscaping', 'Request Details': 'Empty_tree_basin', 'Supervisor District': 1.0, 'Source': 'Voice In', 'XBin': -122.46525666666668, 'YBin': 37.77258066666667}, {'Category': 'Street and Sidewalk Cleaning', 'TimeBin': 135157330.44388878, 'Request Type': 'Sidewalk_Cleaning', 'Request Details': 'Clothing', 'Supervisor District': 1.0, 'Source': 'Voice In', 'XBin': -122.46525666666668, 'YBin': 37.77258066666667}, {'Category': 'Street and Sidewalk Cleaning', 'TimeBin': 135157330.44388878, 'Request Type': 'Sidewalk_Cleaning', 'Request Details': 'Mattress', 'Supervisor District': 1.0, 'Source': 'Voice In', 'XBin': -122.46525666666668, 'YBin': 37.77258066666667}, {'Category': 'Street and Sidewalk Cleaning', 'TimeBin': 135157330.44388878, 'Request Type': 'Sidewalk_Cleaning', 'Request Details': 'Garbage', 'Supervisor District': 1.0, 'Source': 'Voice In', 'XBin': -122.46525666666668, 'YBin': 37.77258066666667}]
[{'Category': 'LARCENY/THEFT', 'TimeBin': 135157330.44388878, 'YBin': 37.77258066666667, 'Descript': 'GRAND THEFT FROM LOCKED AUTO', 'XBin': -122.46525666666668, 'PdDistrict': 'RICHMOND', 'Resolution': 'UNFOUNDED'}, {'Category': 'OTHER OFFENSES', 'TimeBin': 135157330.44388878, 'YBin': 37.77258066666667, 'Descript': 'DRIVERS LICENSE, SUSPENDED OR REVOKED', 'XBin': -122.46525666666668, 'PdDistrict': 'RICHMOND', 'Resolution': 'ARREST, CITED'}, {'Category': 'MISSING PERSON', 'TimeBin': 135157330.44388878, 'YBin': 37.77258066666667, 'Descript': 'MISSING ADULT', 'XBin': -122.46525666666668, 'PdDistrict': 'PARK', 'Resolution': 'LOCATED'}, {'Category': 'MISSING PERSON', 'TimeBin': 135157330.44388878, 'YBin': 37.77258066666667, 'Descript': 'FOUND PERSON', 'XBin': -122.46525666666668, 'PdDistrict': 'PARK', 'Resolution': 'LOCATED'}]
In [106]:
# Get all possible labels for certain categorical data, by looping through the data.
Category_set = set()
Request_Type_set = set()
Request_Details_set = set()
Supervisor_District_set = set()
Source_set = set()
for row_tuple in df.iterrows():
row = row_tuple[1] # a pandas Series
#print row['feature-311']
try:
for dct in eval(row['feature-311']):
#print dct
Category = dct['Category']
Request_Type = dct['Request Type']
Request_Details = dct['Request Details']
Supervisor_District = int(dct['Supervisor District'])
Source = dct['Source']
Category_set.add(Category)
Request_Type_set.add(Request_Type)
Request_Details_set.add(Request_Details)
Supervisor_District_set.add(Supervisor_District)
Source_set.add(Source)
except NameError as e:
continue
print "done"
done
In [107]:
# Get all possible types of 911-reports
set_911 = {}
count = 0
for row_tuple in df.iterrows():
count += 1
if count == "never":
break
row = row_tuple[1] # a pandas Series
#print row['911-reports']
try:
for dct in eval(row['911-reports']):
#print dct
Category = dct['Category']
if Category not in set_911.keys():
set_911[Category] = 0
set_911[Category]+=1
except NameError as e:
continue
print "done"
print set_911
print len(set_911)
# don't get rid of categories that happen rarely, just to see the result of not having enough data
done
{'KIDNAPPING': 10, 'WEAPON LAWS': 17, 'WARRANTS': 82, 'EMBEZZLEMENT': 1, 'BURGLARY': 127, 'SUICIDE': 2, 'DRIVING UNDER THE INFLUENCE': 6, 'ROBBERY': 68, 'SEX OFFENSES, NON FORCIBLE': 2, 'SUSPICIOUS OCC': 81, 'ARSON': 1, 'FORGERY/COUNTERFEITING': 21, 'DRUNKENNESS': 14, 'OTHER OFFENSES': 285, 'FRAUD': 54, 'RECOVERED VEHICLE': 11, 'SEX OFFENSES, FORCIBLE': 12, 'DRUG/NARCOTIC': 90, 'TRESPASS': 17, 'LARCENY/THEFT': 593, 'VANDALISM': 139, 'MISSING PERSON': 124, 'LIQUOR LAWS': 3, 'VEHICLE THEFT': 132, 'STOLEN PROPERTY': 1, 'ASSAULT': 179, 'NON-CRIMINAL': 276, 'DISORDERLY CONDUCT': 8, 'RUNAWAY': 5}
29
In [44]:
print len(Category_set) #
print len(Request_Type_set) #TOO BIG
print len(Request_Details_set) #TOO BIG
print len(Supervisor_District_set)
print len(Source_set)
print Category_set
print Supervisor_District_set
print Source_set
25
421
4120
12
7
set(['Streetlights', 'Abandoned Vehicle', 'MUNI Feedback', 'Graffiti Private Property', 'DPW Volunteer Programs', 'Tree Maintenance', 'Rec and Park Requests', 'Residential Building Request', 'Blocked Street or SideWalk', 'Sidewalk or Curb', 'Temporary Sign Request', 'Sewer Issues', '311 External Request', 'Litter Receptacles', 'Catch Basin Maintenance', 'Street Defects', 'Street and Sidewalk Cleaning', 'Color Curb', 'SFHA Requests', 'Graffiti Public Property', 'Illegal Postings', 'Damaged Property', 'Unpermitted Cab Complaint', 'General Requests', 'Sign Repair'])
set([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11])
set(['Web Self Service', 'Twitter', 'Other Department', 'Voice In', 'e-mail In', 'Open311', 'Integrated Agency'])
In [108]:
# Generate a feature vector given a list of dictionaries. Each dictionary represents one 311 report.
# Regularization should also go here
# @param reports_311: a list of dictionaries. Each dictionary is of the form
# {'Category': string, 'Request Details': string, 'Request Type': string, 'Source': string, 'Supervisor District':string,
# 'TimeBin': float, 'XBin':float, 'YBin':float}
#create map dictionaries that will map each one of the categories from the category sets to an index in feature_vector.
category_map = {}
supervisor_map = {}
source_map = {}
index = 0
for cat in Category_set:
category_map[cat] = index
index += 1
for cat in Supervisor_District_set:
supervisor_map[cat] = index
index += 1
for cat in Source_set:
source_map[cat] = index
index += 1
def generate_feature_vector(reports_311):
# simple feature vector that is just sum of counts of 311 reports of each category
# from (Category, Supervisor District, and Source)
feature_vector = []
for i in xrange(index):
feature_vector.append(0)
for report in reports_311:
# the first 27 features correspond to the number of 311 reports from each category from Category set
# the next 12 features correspond to the number of 311 reports from each category from Supervisor_District set
# the next 9 features correspond to the number of 311 reports from each category from Source set
feature_vector[category_map[report["Category"]]]+=1
feature_vector[supervisor_map[report["Supervisor District"]]]+=1
feature_vector[source_map[report["Source"]]]+=1
return feature_vector
print "done"
done
In [121]:
category_map
Out[121]:
{'311 External Request': 12,
'Abandoned Vehicle': 1,
'Blocked Street or SideWalk': 8,
'Catch Basin Maintenance': 14,
'Color Curb': 17,
'DPW Volunteer Programs': 4,
'Damaged Property': 21,
'General Requests': 23,
'Graffiti Private Property': 3,
'Graffiti Public Property': 19,
'Illegal Postings': 20,
'Litter Receptacles': 13,
'MUNI Feedback': 2,
'Rec and Park Requests': 6,
'Residential Building Request': 7,
'SFHA Requests': 18,
'Sewer Issues': 11,
'Sidewalk or Curb': 9,
'Sign Repair': 24,
'Street Defects': 15,
'Street and Sidewalk Cleaning': 16,
'Streetlights': 0,
'Temporary Sign Request': 10,
'Tree Maintenance': 5,
'Unpermitted Cab Complaint': 22}
In [109]:
def generate_output_vector(reports_911, cat):
ct = 0
for report in reports_911:
if report['Category'] == cat:
#print report['Category']
#print cat
ct += 1
return ct
def generate_output_vector_aggregate(reports_911, cat=None):
return len(reports_911)
In [131]:
# compute f_in
count = 0
df_dict = {}
df_dict['features'] = []
for row_tuple in df.iterrows():
count += 1
if count > 12000000000:
break
row = row_tuple[1] # a pandas Series
dct = eval(row['feature-311'])
#try:
f_in = generate_feature_vector(dct)
#try:
dct2 = eval(row['911-reports'])
#except NameError as e:
# continue
#f_out = generate_output_vector(dct2, cat)
f_out = generate_output_vector_aggregate(dct2)
#print f_in
f_in.append(f_out)
#print f_in
df_dict['features'].append(f_in)
print "done"
done
In [126]:
print df_dict['features'][11]
[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 4]
In [132]:
l_f_in = len(df_dict['features'])
print l_f_in
10000
In [31]:
# compute a list of f_out
count = 0
df_dict_911 = {}
for cat in set_911.keys():
df_dict_911[cat] = []
count = 0
for row_tuple in df.iterrows():
count += 1
if count > 5000000:
break
row = row_tuple[1] # a pandas Series
#try:
dct2 = eval(row['911-reports']) # a list of 911-reports
#print dct2
#except NameError as e:
# continue
f_out = generate_output_vector(dct2, cat)
df_dict_911[cat].append(f_out)
print "done"
done
In [32]:
for key in df_dict_911.keys():
print key
l_911 = len(df_dict_911[key])
if l_911 != l_f_in:
assert False, "error"
KIDNAPPING
WEAPON LAWS
WARRANTS
EMBEZZLEMENT
BURGLARY
FRAUD
DRIVING UNDER THE INFLUENCE
ROBBERY
SEX OFFENSES, NON FORCIBLE
SUSPICIOUS OCC
ARSON
FORGERY/COUNTERFEITING
DRUNKENNESS
OTHER OFFENSES
SUICIDE
RECOVERED VEHICLE
SEX OFFENSES, FORCIBLE
DRUG/NARCOTIC
TRESPASS
LARCENY/THEFT
VANDALISM
NON-CRIMINAL
LIQUOR LAWS
VEHICLE THEFT
STOLEN PROPERTY
ASSAULT
MISSING PERSON
DISORDERLY CONDUCT
RUNAWAY
In [133]:
final_df = pd.DataFrame(df_dict)
final_df.head(5)
print final_df.iloc[11][0]
len(final_df)
[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 4]
Out[133]:
10000
In [134]:
# use the joined table to create a matrix.
# The matrix will be Nx(D+1), where N is the number of unique tuples, and D is the number of features in
# our feature vector. The last column of the matrix corresponds to the feature-911, i.e. the number
# of 911 reports in the given location/time.
import numpy as np
N = len(final_df)
D = final_df['features']
#print type(D[i])
mat = np.zeros([N, len(D[0])])
for i in xrange(N):
mat[i] = D[i]
print mat[11]
[ 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 3. 0.
0. 0. 0. 0. 0. 0. 0. 0. 4. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 4. 0. 0. 0. 4.]
In [135]:
mat.shape
Out[135]:
(10000, 45)
In [136]:
np.savetxt("joined_matrix_features.txt", mat)
In [36]:
ct = 0
for key in df_dict_911.keys():
ct+=1
if ct > 1:
break
print key
print df_dict_911[key]
KIDNAPPING
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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In [37]:
import json
with open('joined_matrix_911.txt', 'w') as outfile:
json.dump(df_dict_911, outfile)
Content source: dolphyin/cs194-16-data_manatees
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