def compare(origin, dest, knn):
"""Compare two cities."""
global ORIGIN
global DEST
origin = 'barcelona' if origin not in c.SHORT_KEY else origin
dest = 'helsinki' if dest not in c.SHORT_KEY else dest
ORIGIN = cn.gather_info(origin, knn, raw_features=True)
DEST = cn.gather_info(dest, knn, raw_features=True)
return f.render_template('cnn.html', origin=origin, dest=dest, knn=knn,
lbbox=c.BBOXES[origin], rbbox=c.BBOXES[dest])
def neighborhoods(origin, dest):
"""Match neighborhoods."""
global ORIGIN
global DEST
origin = 'paris' if origin not in c.SHORT_KEY else origin
dest = 'helsinki' if dest not in c.SHORT_KEY else dest
ORIGIN = cn.gather_info(origin, 1, raw_features=True)
DEST = cn.gather_info(dest, 1, raw_features=True)
return f.render_template('nei.html', origin=origin, dest=dest,
lbbox=c.BBOXES[origin], rbbox=c.BBOXES[dest])
def neighborhoods(origin, dest):
"""Match neighborhoods."""
global ORIGIN
global DEST
origin = 'paris' if origin not in c.SHORT_KEY else origin
dest = 'helsinki' if dest not in c.SHORT_KEY else dest
ORIGIN = cn.gather_info(origin, 1, raw_features=True)
DEST = cn.gather_info(dest, 1, raw_features=True)
return f.render_template('nei.html',
origin=origin,
dest=dest,
lbbox=c.BBOXES[origin],
rbbox=c.BBOXES[dest])
def compare(origin, dest, knn):
"""Compare two cities."""
global ORIGIN
global DEST
origin = 'barcelona' if origin not in c.SHORT_KEY else origin
dest = 'helsinki' if dest not in c.SHORT_KEY else dest
ORIGIN = cn.gather_info(origin, knn, raw_features=True)
DEST = cn.gather_info(dest, knn, raw_features=True)
return f.render_template('cnn.html',
origin=origin,
dest=dest,
knn=knn,
lbbox=c.BBOXES[origin],
rbbox=c.BBOXES[dest])
def show_gold(city, neighborhood):
"""Show ground thruth for the given query."""
global ORIGIN
ORIGIN = cn.gather_info(city, 1, raw_features=True)
return f.render_template('gold.html',
district=neighborhood,
bbox=c.BBOXES[city],
city=city)
def interpret_query(from_city, to_city, region, metric):
"""Load informations about cities and compute useful quantities."""
# Load info of the first city
suffix = '_tsne.mat' if metric == 'emd-tsne' else ''
left = cn.gather_info(from_city+suffix, knn=1,
raw_features='lmnn' not in metric,
hide_category=metric != 'jsd')
left_infos = load_surroundings(from_city)
left_support = features_support(left['features'])
# Compute info about the query region
center, radius, _, contains = polygon_to_local(from_city, region)
query = describe_region(center, radius, contains, left_infos[0], left)
features, times, weights, vids = query
# print('{} venues in query region.'.format(len(vids)))
venue_proportion = 1.0*len(vids) / left['features'].shape[0]
# And use them to define the metric that will be used
theta = np.ones((1, left['features'].shape[1]))
theta = np.array([[0.0396, 0.0396, 0.2932, 0.0396, 0.0396, 0.0396,
0.0396, 0.3404, 0.0396, 0.0396, 0.0396, 0.0396,
0.0396, 0.3564, 0.0396, 0.3564, 0.0396, 0.3564,
0.3564, 0.3564, 0.0396, 0.0396, 0.0396, 0.0396,
0.3564, 0.0396, 0.0396, 0.0396, 0.0396, 0.0396,
0.0396]])
ltheta = len(theta.ravel())*[1, ]
if 'emd' in metric:
from emd import emd
from emd_dst import dist_for_emd
if 'tsne' in metric:
from specific_emd_dst import dst_tsne as dist_for_emd
if 'itml' in metric:
from specific_emd_dst import dst_itml as dist_for_emd
query_num = features_as_lists(features)
@profile
def regions_distance(r_features, r_weigths):
if len(r_features) >= MAX_EMD_POINTS:
return 1e20
return emd((query_num, map(float, weights)),
(r_features, map(float, r_weigths)),
lambda a, b: float(dist_for_emd(a, b, ltheta)))
elif 'cluster' in metric:
from scipy.spatial.distance import cdist
query_num = weighted_clusters(features, NB_CLUSTERS, weights)
def regions_distance(r_features, r_weigths):
r_cluster = weighted_clusters(r_features, NB_CLUSTERS, r_weigths)
costs = cdist(query_num, r_cluster).tolist()
return min_cost(costs)
elif 'leftover' in metric:
@profile
def regions_distance(r_features, second_arg):
r_weigths, idx = second_arg
emd_leftover.write_matlab_problem(features, weights, r_features,
r_weigths, idx)
return -1
else:
query_num = features_as_density(features, weights, left_support)
@profile
def regions_distance(r_density, r_global):
"""Return distance of a region from `query_num`."""
return proba_distance(query_num, times, r_density, r_global,
theta)
# Load info of the target city
right = cn.gather_info(to_city+suffix, knn=2,
raw_features='lmnn' not in metric,
hide_category=metric != 'jsd')
right_infos = load_surroundings(to_city)
minx, miny, maxx, maxy = right_infos[1]
right_city_size = (maxx - minx, maxy - miny)
right_support = features_support(right['features'])
global RIGHT_SUPPORT
RIGHT_SUPPORT = right_support
# given extents, compute threshold of candidate
threshold = 0.7 * venue_proportion * right['features'].shape[0]
right_desc = [right_city_size, right_support, right, right_infos]
return [left, right, right_desc, regions_distance, vids, threshold]
def show_gold(city, neighborhood):
"""Show ground thruth for the given query."""
global ORIGIN
ORIGIN = cn.gather_info(city, 1, raw_features=True)
return f.render_template('gold.html', district=neighborhood,
bbox=c.BBOXES[city], city=city)
def interpret_query(from_city, to_city, region, metric):
"""Load informations about cities and compute useful quantities."""
# Load info of the first city
suffix = '_tsne.mat' if metric == 'emd-tsne' else ''
left = cn.gather_info(from_city + suffix,
knn=1,
raw_features='lmnn' not in metric,
hide_category=metric != 'jsd')
left_infos = load_surroundings(from_city)
left_support = features_support(left['features'])
# Compute info about the query region
center, radius, _, contains = polygon_to_local(from_city, region)
query = describe_region(center, radius, contains, left_infos[0], left)
features, times, weights, vids = query
# print('{} venues in query region.'.format(len(vids)))
venue_proportion = 1.0 * len(vids) / left['features'].shape[0]
# And use them to define the metric that will be used
theta = np.ones((1, left['features'].shape[1]))
theta = np.array([[
0.0396, 0.0396, 0.2932, 0.0396, 0.0396, 0.0396, 0.0396, 0.3404, 0.0396,
0.0396, 0.0396, 0.0396, 0.0396, 0.3564, 0.0396, 0.3564, 0.0396, 0.3564,
0.3564, 0.3564, 0.0396, 0.0396, 0.0396, 0.0396, 0.3564, 0.0396, 0.0396,
0.0396, 0.0396, 0.0396, 0.0396
]])
ltheta = len(theta.ravel()) * [
1,
]
if 'emd' in metric:
from emd import emd
from emd_dst import dist_for_emd
if 'tsne' in metric:
from specific_emd_dst import dst_tsne as dist_for_emd
if 'itml' in metric:
from specific_emd_dst import dst_itml as dist_for_emd
query_num = features_as_lists(features)
@profile
def regions_distance(r_features, r_weigths):
if len(r_features) >= MAX_EMD_POINTS:
return 1e20
return emd((query_num, map(float, weights)),
(r_features, map(float, r_weigths)),
lambda a, b: float(dist_for_emd(a, b, ltheta)))
elif 'cluster' in metric:
from scipy.spatial.distance import cdist
query_num = weighted_clusters(features, NB_CLUSTERS, weights)
def regions_distance(r_features, r_weigths):
r_cluster = weighted_clusters(r_features, NB_CLUSTERS, r_weigths)
costs = cdist(query_num, r_cluster).tolist()
return min_cost(costs)
elif 'leftover' in metric:
@profile
def regions_distance(r_features, second_arg):
r_weigths, idx = second_arg
emd_leftover.write_matlab_problem(features, weights, r_features,
r_weigths, idx)
return -1
else:
query_num = features_as_density(features, weights, left_support)
@profile
def regions_distance(r_density, r_global):
"""Return distance of a region from `query_num`."""
return proba_distance(query_num, times, r_density, r_global, theta)
# Load info of the target city
right = cn.gather_info(to_city + suffix,
knn=2,
raw_features='lmnn' not in metric,
hide_category=metric != 'jsd')
right_infos = load_surroundings(to_city)
minx, miny, maxx, maxy = right_infos[1]
right_city_size = (maxx - minx, maxy - miny)
right_support = features_support(right['features'])
global RIGHT_SUPPORT
RIGHT_SUPPORT = right_support
# given extents, compute threshold of candidate
threshold = 0.7 * venue_proportion * right['features'].shape[0]
right_desc = [right_city_size, right_support, right, right_infos]
return [left, right, right_desc, regions_distance, vids, threshold]