def process_trip(x, start_time):
tt = time.localtime(start_time)
data = [tt.tm_wday, tt.tm_hour]
# distance from the center till cutting point
v_mn = 0
head = 0
if len(x) > 1:
v_mn = haversine_distance(x[0, :], x[-1, :])[0]
head = heading(x[0, :], x[-1, :])
# distance from the center till cutting point
d_st = haversine_distance(x[0, :], CITY_CENTER)
h_st = heading(x[0, :], CITY_CENTER[0])
data += [x[-1, 0], x[-1, 1], d_st, h_st, v_mn, head]
return data
def distance_intra_cluster(transformed_df, main_df, centroids):
zones = ["D1", "D2", "D3", "D4", "D5", "D6"]
centroids_map = dict(zip(zones, centroids))
centroids_df = pd.DataFrame.from_dict(
centroids_map,
orient="index",
columns=["lat_centroids", "lon_centroids"])
centroids_df = centroids_df.reset_index().rename(
columns={"index": "zones"})
unpivoted_df = pd.melt(transformed_df,
id_vars=['Id_Cliente'],
var_name="zones")
unpivoted_df = unpivoted_df[unpivoted_df.value > 0]
joined_df = unpivoted_df.merge(main_df[["Id_Cliente", "lat", "lon"]], on="Id_Cliente", how = "left")\
.merge(centroids_df, on="zones", how = "left")
joined_df["manhattan"] = manhattan_distance(joined_df["lat"],
joined_df["lon"],
joined_df["lat_centroids"],
joined_df["lon_centroids"])
joined_df["haversine"] = haversine_distance(joined_df["lat"],
joined_df["lon"],
joined_df["lat_centroids"],
joined_df["lon_centroids"])
print("Manhattan Distance")
print(joined_df.groupby("zones")["manhattan"].sum())
print("Haversine Distance")
print(joined_df.groupby("zones")["haversine"].sum())
return (joined_df["manhattan"].sum(), joined_df["haversine"].sum())
def process_trip(x, start_time):
tt = time.localtime(start_time)
data = [tt.tm_wday, tt.tm_hour]
# distance from the center till cutting point
d_st = haversine_distance(x, CITY_CENTER)
head = heading(x, CITY_CENTER[0])
data += [x[0], x[1], d_st, head]
return data
def _compute_or_fetch(self, code_one, loc_one, code_two, loc_two):
"""Computes the distance between two languages or fetches it."""
key_one = (code_one, code_two)
key_two = (code_two, code_one)
if key_one in self._distance_cache or key_two in self._distance_cache:
return self._distance_cache[key_one]
else:
dist = utils.haversine_distance(loc_one, loc_two)
self._distance_cache[key_one] = dist
self._distance_cache[key_two] = dist
return dist
def process_trip(x, start_time):
tt = time.localtime(start_time)
data = [tt.tm_wday, tt.tm_hour]
# cumulative sum of distance
d_cs = 0
vcar = 0
vmed = 0
head = 0
if x.shape[0] > 1:
d1 = haversine_distance(x[:-1, :], x[1:, :])
d_cs = np.sum(d1)
vmed = np.median(d1)
vcar = d1[-1]
head = heading(x[-2, :], x[-1, :])
# distance from the center till cutting point
d_st = haversine_distance(x[0, :], CITY_CENTER)[0]
h_st = heading(x[0, :], CITY_CENTER[0])
d_cut = haversine_distance(x[-1, :], CITY_CENTER)[0]
h_cut = heading(CITY_CENTER[0], x[-1, :])
data += [
x.shape[0], x[0, 0], x[0, 1], x[-1, 0], x[-1, 1], d_st, h_st, d_cut,
h_cut, d_cs, vmed, vcar, head
]
return data
def SearchNearestPharmacy(cls, currentLocation: Dict, range: int,
limit: int) -> tuple:
if not {"latitude", "longitude"} <= currentLocation.keys():
return {
"message":
"Validation error: currentLocation must contain 'latitude' and 'longitude'"
}, 422
uri = env("DATA_URL")
try:
res = requests.get(uri, timeout=1)
except requests.ConnectionError:
logger.error("Connection error")
return {"message": "Connection Error"}, 503
except Exception as e:
logger.error(str(e))
return {"message": str(e)}, 500
if res.status_code != 200:
logger.error(res.status_code)
return {"message": "Service error"}, res.status_code
data = res.json()['features']
pharmacies_distance = []
for key, pharmacy in enumerate(data):
coordinates = pharmacy['geometry']['coordinates']
pharmacy_name = pharmacy['properties']['Descrizione']
distance = utils.haversine_distance(currentLocation['latitude'],
currentLocation['longitude'],
coordinates[1], coordinates[0])
if distance <= range:
pharmacies_distance.append({
"name": pharmacy_name,
"distance": distance,
"location": {
"latitude": coordinates[1],
"longitude": coordinates[0]
}
})
if len(pharmacies_distance) == 0:
return "No resources", 404
sorted_pharmacies = sorted(pharmacies_distance,
key=lambda k: k['distance'])
sorted_pharmacies = sorted_pharmacies[:limit] if limit < len(
sorted_pharmacies) else sorted_pharmacies
return {"pharmacies": sorted_pharmacies}, 200
def match_telemetry(self):
"""
Match visitors telemetry fields
:return score: calculated telemetry score based on all attributes
:return ip_timing_red_flag: True if IP timing/geographic location values indicate it can't
be the same person
"""
ip_timing_red_flag = False
match_scores = []
for prev_v in self.prev_vs:
distance_scores, ip_matches = [], []
for previous_ip_data in prev_v["ips"]:
for new_ip_data in self.new_v["ips"]:
distance = haversine_distance(previous_ip_data["props"], new_ip_data["props"])
time_delta = timestamp_difference(
previous_ip_data["updated_at"], new_ip_data["updated_at"]
)
ip_matches.append(exact_match(previous_ip_data["ip"], new_ip_data["ip"]))
if distance > 10 and (distance / time_delta) > self.MAX_PLAUSIBLE_SPEED:
ip_timing_red_flag = True
elif distance < 10:
distance_scores.append(1)
else:
distance_scores.append(min(1000, distance) / 1000)
results = {
"ip_match": max(ip_matches),
"geographic_proximity": 1
- (sum(distance_scores) / len(distance_scores)), # average distance score
"creation_time_proximity": 1
- min(
1,
timestamp_difference(
prev_v["visitors"]["createdAt"], self.new_v["visitors"]["createdAt"]
)
/ 86400, # seconds in a day
),
"visitor_age_proximity": 1
- age_difference(
prev_v["visitors"]["createdAt"], self.new_v["visitors"]["createdAt"]
),
}
match_scores.append(generate_match_score(results, self.weights["telemetry"]))
return {"score": max(match_scores), "ip_timing_red_flag": ip_timing_red_flag}
def find_close_languages(lat1, lng1, languages, distance_cache):
"""Given latitude/longitude coordinates finds the nearest language."""
close_language_indices = []
for i, language in enumerate(languages):
lat2 = language["latitude"]
lng2 = language["longitude"]
loc1 = (float(lat1), float(lng1))
loc2 = (float(lat2), float(lng2))
if (loc1, loc2) not in distance_cache:
dist = utils.haversine_distance((float(lat1), float(lng1)),
(float(lat2), float(lng2)))
distance_cache[(loc1, loc2)] = dist
distance_cache[(loc2, loc1)] = dist
else:
dist = distance_cache[(loc1, loc2)]
if dist < FLAGS.close_enough:
close_language_indices.append(i)
return close_language_indices
def get_user_shop_distance(result):
result['feature_user_shop_lon_sub'] = (result['user_longitude'] -
result['shop_longitude'])
result['feature_user_shop_lat_sub'] = (result['user_latitude'] -
result['shop_latitude'])
result['feature_user_shop_lon_sub_abs'] = abs(
result['feature_user_shop_lon_sub'])
result['feature_user_shop_lat_sub_abs'] = abs(
result['feature_user_shop_lat_sub'])
result['feature_user_shop_uclidean_dis'] = euclidean_distance(
result['user_latitude'], result['user_longitude'],
result['shop_latitude'], result['shop_longitude'])
result['feature_user_shop_haversine_dis'] = haversine_distance(
result['user_latitude'], result['user_longitude'],
result['shop_latitude'], result['shop_longitude'])
result['feature_user_shop_manhattan_dis'] = manhattan_distance(
result['user_latitude'], result['user_longitude'],
result['shop_latitude'], result['shop_longitude'])
return result
def get_user_shop_average_distance(refer, result):
shop_longitude = refer.groupby(['shop_id'],
as_index=False)['longitude'].agg(
{'shop_average_longitude': 'mean'})
shop_latitude = refer.groupby(['shop_id'], as_index=False)['latitude'].agg(
{'shop_average_latitude': 'mean'})
result = pd.merge(result, shop_longitude, on=['shop_id'], how='left')
result = pd.merge(result, shop_latitude, on=['shop_id'], how='left')
result['feature_user_shop_aver_uclidean_dis'] = euclidean_distance(
result['user_latitude'], result['user_longitude'],
result['shop_average_latitude'], result['shop_average_longitude'])
result['feature_user_shop_aver_haversine_dis'] = haversine_distance(
result['user_latitude'], result['user_longitude'],
result['shop_average_latitude'], result['shop_average_longitude'])
result['feature_user_shop_aver_manhattan_dis'] = manhattan_distance(
result['user_latitude'], result['user_longitude'],
result['shop_average_latitude'], result['shop_average_longitude'])
del result['shop_average_longitude']
del result['shop_average_latitude']
return result
import os
import time
import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestRegressor
from utils import haversine_distance
DATA_DIR = '../data'
t0 = time.time()
for filename in ['train_pp_N2.csv', 'train_pp_N3.csv', 'train_pp_N1.csv']:
print('reading training data from %s ...' % filename)
df = pd.read_csv(os.path.join(DATA_DIR, filename))
d1 = haversine_distance(df[['xs', 'ys']].values, df[['xe', 'ye']].values)
# create training set
y = np.log((df['len'] - 1) * 15)
# remove non-predictive features
df.drop(['CALL_TYPE', 'TAXI_ID', 'xe', 'ye', 'len'], axis=1, inplace=True)
X = np.array(df, dtype=np.float)
# clean data by removing long distance tracks
th1 = np.percentile(d1, [99.9])
X = X[(d1 < th1), :]
y = y[(d1 < th1)]
print('training a random forest regressor ...')
# Initialize the famous Random Forest Regressor from scikit-learn
clf = RandomForestRegressor(n_estimators=200, n_jobs=3, random_state=21)
def getDistanceTo(self, baum):
# the returned distance between this and the given baum (in metre)
return 1000 * haversine_distance(self.latitude, self.longitude,
baum.latitude, baum.longitude)
if not os.path.isfile(filename):
continue
df = pd.read_csv(filename)
if df.shape[0] < 1000:
print('skipping key point %i (%i)' % (id_, df.shape[0]))
continue
# factorize categorical columns in training set
#df['CALL_TYPE'], ct_index = pd.factorize(df['CALL_TYPE'])
#df = df[df['CALL_TYPE'] == 0] # A=2, B=1, C=0
# fill all NaN values with -1
#df = df.fillna(-1)
# remove long distance
d1 = haversine_distance(df[['xs', 'ys']], df[['xe', 'ye']])
th1 = np.percentile(d1, [99.9])
df = df.loc[d1 < th1]
y = np.ravel(np.log(df['len']*15 + 1))
df.drop(['CALL_TYPE', 'TAXI_ID', 'xe', 'ye', 'len'], axis=1, inplace=True)
X = np.array(df, dtype=np.float)
print('training classifier of key point %i (sz=%i) ...' % (id_, X.shape[0]))
# Initialize the famous Random Forest Regressor from scikit-learn
clf = RandomForestRegressor(n_estimators=200, n_jobs=3, random_state=21)
clf.fit(X, y)
pred_rf = clf.predict(X_tst[id_:id_+1, :])
clf = GradientBoostingRegressor(n_estimators=200, max_depth=3, random_state=21)
clf.fit(X, y)