In [1]:
import re, json, random
In [2]:
features_file = open('features.txt', 'r')
features = features_file.read().lower()
features = re.sub(';$', '', features)
features = re.split(';', features)
print features
['downtown berkeley', 'studio', 'bart', 'gourmet ghetto', 'laundry', 'fireplace', 'hardwood', 'carpet', 'remodeled', 'smoke free', 'fully furnished', 'soda hall', 'no pet', 'no bike', 'bathtub', 'dishwasher', 'garage', 'carpeted floor', 'granite', 'garbage disposal', 'free wifi', 'elevator', 'bike rack', 'balcony', 'no parking']
In [3]:
def parseRoomCount(string):
if string == '1' or string == 'one':
return 1
elif string == '2' or string == 'two':
return 2
elif string == '3' or string == 'three':
return 3
elif string == '4' or string == 'four':
return 4
elif string == '5' or string == 'five':
return 5
elif string == '6' or string == 'six':
return 6
elif string == '7' or string == 'seven':
return 7
elif string == '8' or string == 'eight':
return 8
elif string == '9' or string == 'nine':
return 9
elif string == '0' or string == 'ten':
return 0
else:
return None
In [9]:
from math import sin, cos, sqrt, atan2, radians
def latlon_dist(lat2, lon2):
# approximate radius of earth in km
R = 6373.0
lat1 = radians(37.8719)
lon1 = radians(-122.2585)
lat2 = radians(lat2)
lon2 = radians(lon2)
dlon = lon2 - lon1
dlat = lat2 - lat1
a = sin(dlat / 2)**2 + cos(lat1) * cos(lat2) * sin(dlon / 2)**2
c = 2 * atan2(sqrt(a), sqrt(1 - a))
distance = R * c
return distance * 0.621371
print(latlon_dist(37.864153, -122.259464))
0.538011015371
In [120]:
design_matrix = []
cooked_labels = []
all_data = []
postings = []
ct = 0
for asdf in range(0, 10):
json_file = open('data'+str(asdf*100)+'.json', 'r')
input_data = json.load(json_file)
for posting in input_data:
details = input_data[posting]
description = details['features'].lower()
# Count binary features for existence
sample_features = []
for feature in features:
if feature in description:
sample_features.append(1)
else:
sample_features.append(0)
# Count quantitative features
# Bedroom count
bedroom_count = -1
bedroom_keywords = re.findall('\S+?(?=\sbedroom|\sbed|\sbr)s?|(?<=bedroom)s?.+?\S+', description)
if bedroom_keywords:
for i in bedroom_keywords:
count = parseRoomCount(i)
if count != None:
bedroom_count = count
break
sample_features.append(bedroom_count)
# Bathroom count
bathroom_count = -1
bathroom_keywords = re.findall('\S+?(?=\sbathroom|\sbath|\sba)s?|(?<=bathroom)s?.+?\S+', description)
if bathroom_keywords:
for i in bathroom_keywords:
count = parseRoomCount(i)
if count != None:
bathroom_count = count
break
sample_features.append(bathroom_count)
# Square feet
square_feet = -1
square_feet_keywords = re.findall('\d+(?=ft2)', details['size'])
if square_feet_keywords:
square_feet = float(square_feet_keywords[0])
sample_features.append(square_feet)
# Add other numerical values
# Price
if details['price'] == 'n/a':
continue
price = float(re.sub('^\$', '', details['price']))
sample_features.append(price)
# Longitude
try:
longitude = float(details['longitude'])
except ValueError:
longitude = 360.0
continue
sample_features.append(longitude)
# Latitude
try:
latitude = float(details['latitude'])
except ValueError:
latitude = 180.0
continue
sample_features.append(latitude)
dist = latlon_dist(latitude, longitude)
sample_features.append(dist)
if bedroom_count == -1:
continue
design_matrix.append(sample_features)
# Cooking training data
if bedroom_count >= 2 and bedroom_count <= 3 and price <= 4000 and price >= 2300 and dist < 2:
if random.random() < 1:
cooked_labels.append(1)
ct+=1
else:
cooked_labels.append(0)
else:
cooked_labels.append(0)
postings.append(posting)
all_data.append(details)
print asdf, ct
print len(cooked_labels)
labels = cooked_labels
0 9
1 17
2 21
3 33
4 45
5 55
6 63
7 74
8 83
9 84
444
In [91]:
print design_matrix
[[0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, -1, 2255.0, -122.260344, 37.877106, 0.37361198844163324], [0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, -1, 2595.0, -122.265387, 37.874582, 0.4189733515480534], [0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 3, -1, 1100.0, 3100.0, -122.307422, 37.911396, 3.8173179441096323], [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 2, -1, 1350.0, 3500.0, -122.286158, 37.843241, 2.4902464500284003], [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, -1, 818.0, 3034.0, -122.288969, 37.839369, 2.7963907433340007], [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, -1, 640.0, 1850.0, -122.252601, 37.833289, 2.687946357957304], [0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, -1, 1423.0, 4982.0, -122.28899, 37.83936, 2.797572379374738], [0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 2, -1, 1185.0, 3300.0, -122.292621, 37.834482, 3.1867547272314356], [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, -1, -1, 2895.0, -122.263345, 37.864644, 0.5669044282836505], [0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 700.0, 1300.0, -122.27428, 37.849526, 1.7699476107163707], [1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, -1, 1600.0, -122.260796, 37.852308, 1.3598842183213067], [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 2, 1, 800.0, 3950.0, -122.261181, 37.883152, 0.7913160293197306], [0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, -1, -1, 3595.0, -122.26068, 37.853126, 1.303005857820847], [0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, -1, 1700.0, -122.275032, 37.823889, 3.4387557534913222], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, -1, 540.0, 2864.0, -122.279692, 37.870566, 1.1598814423263615], [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 1, -1, 3995.0, -122.265498, 37.864363, 0.645866093350808], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, -1, 1239.0, 4655.0, -122.288947, 37.839335, 2.797567530410993], [0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 2, 1, 1263.0, 3250.0, -122.289076, 37.847443, 2.375079634055397], [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, -1, -1, 2995.0, -122.26018, 37.864126, 0.5450626773915009], [0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, -1, -1, 2450.0, -122.26328, 37.863826, 0.6159714072374505], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 3, -1, -1, 4495.0, -122.290577, 37.856017, 2.0660256913026083], [0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, -1, 1550.0, -122.300249, 37.894971, 2.7801343786911654], [0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 1073.0, 3435.0, -122.29558, 37.844026, 2.793853855086233], [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 3, -1, 957.0, 3450.0, -122.25852, 37.862708, 0.6353059501147497], [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, -1, -1, 2995.0, -122.26018, 37.864126, 0.5450626773915009], [0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, -1, -1, 2000.0, -122.297623, 37.889854, 2.4687510765699394], [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 1089.0, 3566.0, -122.29558, 37.844026, 2.793853855086233], [0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, -1, -1, 1800.0, -122.253413, 37.816599, 3.832199029278835], [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, -1, 635.0, 2530.0, -122.294186, 37.843953, 2.742815912547232], [1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 4, 3, -1, 5495.0, -122.255079, 37.887725, 1.1095522292276143], [0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 2, -1, -1, 3495.0, -122.26488, 37.878125, 0.5534060269427621], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, -1, 710.0, 2200.0, -122.30607, 37.837751, 3.5084893370037173], [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 2, 1, 935.0, 3070.0, -122.294186, 37.843953, 2.742815912547232], [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, -1, 484.0, 3347.0, -122.271023, 37.87093, 0.6865200785493523], [0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 1, -1, 2500.0, -122.2223, 37.8471, 2.6153318639582612], [0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, -1, -1, 2895.0, -122.25698, 37.863626, 0.5778398433449984], [0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, -1, 1340.0, 3600.0, -122.280896, 37.845805, 2.178612998947731], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 2, -1, 1286.0, 3957.0, -122.28899, 37.83936, 2.797572379374738], [0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 2, 1400.0, 6395.0, -122.264748, 37.865429, 0.562348826531816], [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 1, -1, 2650.0, -122.26698, 37.854626, 1.2804213081355134], [0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 2, 2, 1170.0, 2895.0, -122.30702, 37.89322, 3.029320694189349], [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, -1, 1175.0, 3824.0, -122.288915, 37.839335, 2.7965307649314797], [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, -1, 635.0, 2518.0, -122.294186, 37.843953, 2.742815912547232], [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, -1, 671.0, 4186.0, -122.257, 37.8664, 0.3888426019225337], [0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 1, 700.0, 2195.0, -122.250249, 37.82375, 3.3582150804227284], [0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 3, 2, 1200.0, 5200.0, -122.27958, 37.880292, 1.288010784131975], [0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, -1, -1, 2195.0, -122.263256, 37.864657, 0.5638585852039746], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, -1, 449.0, 1750.0, -122.280373, 37.831278, 3.0508094426133274], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, -1, 765.0, 2790.0, -122.288979, 37.839335, 2.7986050014284394], [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 2, 1400.0, 2995.0, -122.223394, 37.853255, 2.308682946348871], [0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 3, -1, -1, 4395.0, -122.26328, 37.863826, 0.6159714072374505], [0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 3, 2, 1000.0, 2795.0, -122.300827, 37.903449, 3.175714451482837], [0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 650.0, 2250.0, -122.26728, 37.878625, 0.6674421077263757], [1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 3, 2, 1070.0, 4585.0, -122.27248, 37.863326, 0.965910758337759], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, -1, 2000.0, 2550.0, -122.2655, 37.8784, 0.5896308192944031], [0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 3, -1, -1, 4295.0, -122.26268, 37.860226, 0.8384636042996033], [0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 818.0, 2495.0, -122.2804, 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0.8057659368767024], [0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, -1, -1, 1895.0, -122.253799, 37.820955, 3.5303985111575256], [1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, -1, -1, 2395.0, -122.2655, 37.8784, 0.5896308192944031], [0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 2, -1, 1925.0, 5200.0, -122.27808, 37.905625, 2.563932631417665], [0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 560.0, 1600.0, -122.26938, 37.877525, 0.7095572993819623], [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 2, -1, -1, 3200.0, -122.2804, 37.8365, 2.722957482358676], [0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 2, 2, 1600.0, 3750.0, -122.206078, 37.887048, 3.0453838658880086], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 4, -1, 1350.0, 5000.0, -122.254331, 37.865385, 0.5044756096568052], [0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, -1, -1, 2500.0, -122.279637, 37.854672, 1.657702867075328], [0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, -1, 1400.0, 4200.0, -122.26138, 37.878625, 0.4906367895651268], [0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, -1, 850.0, 3000.0, -122.28768, 37.869626, 1.5997764556015581], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 7, -1, -1, 12995.0, -122.26908, 37.873925, 0.593944885893078], [0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, -1, 2595.0, -122.265365, 37.874667, 0.42053592962720815], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, -1, 700.0, 1599.0, -122.282993, 37.821904, 3.705023354807654], [0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 2, -1, -1, 2695.0, -122.269411, 37.874776, 0.6275852502373025], [0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 1, -1, 2695.0, -122.249637, 37.824819, 3.289757741718362], [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 3, -1, 1850.0, 3598.0, -122.286403, 37.847911, 2.251053443737407], [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, -1, 1800.0, -122.2749, 37.863, 1.085847002903457], [1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 2, -1, -1, 2795.0, -122.272548, 37.873431, 0.7736978984603334], [1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 3, 2, 1300.0, 5480.0, -122.265472, 37.865164, 0.6012051470587022], [1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 3, -1, -1, 1550.0, -122.269678, 37.86573, 0.7441793154950167], [0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 1, 700.0, 2295.0, -122.24978, 37.824227, 3.329110229153125], [0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 1, 800.0, 2595.0, -122.275429, 37.83426, 2.760660825060471], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, -1, 1450.0, 3500.0, -122.213978, 37.835826, 3.481303619445465], [0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, -1, 1995.0, -122.2488, 37.82543, 3.255109306524348], [0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, -1, -1, 1850.0, -122.281888, 37.908569, 2.837333819728785], [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 2, -1, 3958.0, -122.285893, 37.823294, 3.677042974715509], [0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 2, 2, -1, 2400.0, -122.2954, 37.89, 2.3700097472449224], [0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 1, -1, 2695.0, -122.24994, 37.824067, 3.338822497647455], [1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 4, 1, -1, 950.0, -122.270049, 37.865687, 0.7625265226257776], [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 3, 1, 1400.0, 2950.0, -122.273615, 37.822126, 3.537654327777779], [0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, -1, 1200.0, 2400.0, -122.28688, 37.900625, 2.517544579392086], [0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, -1, 2295.0, -122.24978, 37.824227, 3.329110229153125], [0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 5, -1, 5350.0, 9500.0, -122.212257, 37.894092, 2.9522637287228344], [0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, -1, 2095.0, -122.300496, 37.838052, 3.2749001479206563], [0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, -1, 1100.0, 3500.0, -122.26483, 37.849826, 1.5642557551155132], [0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 1, 1060.0, 3700.0, -122.290514, 37.888741, 2.098770842073341], [0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 2, 1, 1000.0, 3300.0, -122.274013, 37.841038, 2.2948715494031084], [0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 2, 1, -1, 2200.0, -122.27608, 37.858563, 1.3303378674251687], [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 7, -1, -1, 10500.0, -122.268705, 37.878532, 0.721156269613086], [0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, -1, 1200.0, 2400.0, -122.28688, 37.900625, 2.517544579392086], [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 2, -1, -1, 3000.0, -122.281952, 37.843393, 2.3494018264047316], [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, -1, 850.0, -122.280836, 37.872499, 1.2193135279112144], [0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 650.0, 1600.0, -122.272702, 37.874314, 0.792584816984267], [0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, -1, 2095.0, -122.300496, 37.838052, 3.2749001479206563], [0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 676.0, 2330.0, -122.279549, 37.831429, 3.0238399948678882], [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 2, -1, 7000.0, -122.27368, 37.872425, 0.8289873970692695], [0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 2, 1, 700.0, 1800.0, -122.25798, 37.816627, 3.820298414262621], [0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 3, 2, 1288.0, 3600.0, -122.279656, 37.831412, 3.0271492591819573], [0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 2, -1, -1, 1895.0, -122.26308, 37.825727, 3.2010205191085146], [0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 2, 1, 900.0, 2975.0, -122.272789, 37.838581, 2.4312774137116873], [0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 2, 1, -1, 2200.0, -122.27608, 37.858563, 1.3303378674251687], [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, -1, 3100.0, -122.265515, 37.876124, 0.4813537490307525], [0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, -1, -1, 2175.0, -122.2804, 37.8365, 2.722957482358676], [0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 3, 1, -1, 2695.0, -122.277296, 37.81887, 3.806024233936354], [0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 2, 1, 750.0, 2100.0, -122.272702, 37.874314, 0.792584816984267], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 1, 874.0, 3130.0, -122.301595, 37.865903, 2.3875408373862714], [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, -1, 1325.0, 4250.0, -122.2954, 37.89, 2.3700097472449224], [0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 2, -1, 1399.0, 3350.0, -122.279656, 37.831412, 3.0271492591819573], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 500.0, 1600.0, -122.27048, 37.854526, 1.3671998992621652]]
In [92]:
# Cooking training data
cooked_file = open('rnd_preference.csv', 'w')
cooked_file.write('Link,Label\n')
i = 0
for posting in postings:
cooked_file.write(str(posting) + ',' + str(cooked_labels[i]) + '\n')
i += 1
cooked_file.close()
In [105]:
import numpy as np
import matplotlib.pyplot as plt
from sklearn import linear_model, svm, datasets, cross_validation
%matplotlib inline
In [94]:
X = np.array(design_matrix)
y = np.array(labels)
In [95]:
logr = linear_model.LogisticRegressionCV()
logr.fit(X, y)
print logr.C_
[ 0.0001]
In [96]:
probs = logr.predict_proba(X)[:, 1]
np.sum(np.abs(probs - y))
Out[96]:
128.82071579317821
In [115]:
S = svm.SVC(class_weight = "balanced", probability = True)
S.fit(X, y)
probs = S.predict_proba(X)[:, 1]
In [119]:
order = np.argsort(probs)
posts = np.array(postings)
print posts[order[:10]], posts[order[-10:]]
print np.array(labels)[order[-10:]]
print np.array(labels)[order[:10]]
print probs[order[-10:]], probs[order[:10]]
[u'https://sfbay.craigslist.org/eby/apa/5501823690.html'
u'https://sfbay.craigslist.org/eby/apa/5503520226.html'
u'https://sfbay.craigslist.org/eby/apa/5473978305.html'
u'https://sfbay.craigslist.org/eby/apa/5506203549.html'
u'https://sfbay.craigslist.org/eby/apa/5506198514.html'
u'https://sfbay.craigslist.org/eby/apa/5509559468.html'
u'https://sfbay.craigslist.org/eby/apa/5482809697.html'
u'https://sfbay.craigslist.org/eby/apa/5514486605.html'
u'https://sfbay.craigslist.org/eby/apa/5487551600.html'
u'https://sfbay.craigslist.org/eby/apa/5519361835.html'] [u'https://sfbay.craigslist.org/eby/apa/5512455140.html'
u'https://sfbay.craigslist.org/eby/apa/5512622882.html'
u'https://sfbay.craigslist.org/eby/apa/5519224401.html'
u'https://sfbay.craigslist.org/eby/apa/5504915723.html'
u'https://sfbay.craigslist.org/eby/apa/5493056353.html'
u'https://sfbay.craigslist.org/eby/apa/5507121228.html'
u'https://sfbay.craigslist.org/eby/apa/5508361510.html'
u'https://sfbay.craigslist.org/eby/apa/5501894926.html'
u'https://sfbay.craigslist.org/eby/apa/5512648462.html'
u'https://sfbay.craigslist.org/eby/apa/5476836940.html']
[1 1 1 1 1 1 1 1 1 1]
[0 0 0 0 0 0 0 0 0 0]
[ 0.87859254 0.87860369 0.87861376 0.87861387 0.8786165 0.87861662
0.87925212 0.88108135 0.88480319 0.89325069] [ 0.03974292 0.03974299 0.0414214 0.04168969 0.04168969 0.04197545
0.04200889 0.04239366 0.04242302 0.04247682]
In [130]:
import json
order = np.argsort(probs)[::-1]
with open('data.json', 'w') as outfile:
json.dump({"probs": probs[order].tolist(),
"design_matrix": X[order].tolist(),
"postings": np.array(all_data)[order].tolist(),
"bless_down": np.array(postings)[order].tolist()}, outfile)
In [134]:
print(design_matrix[0])
print(len(design_matrix[0]))
[0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, -1, 2255.0, -122.260344, 37.877106, 0.37361198844163324]
32
In [ ]:
Content source: wyndwarrior/HouseRank
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