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import os
import json
import joblib
import pandas as pd
import numpy as np
import reverse_geocoder
from os.path import join, dirname
class QuadTree(object):
def __init__(
self, data, mins=None, maxs=None, id="", depth=3, min_split=0, do_split=1000
):
self.id = id
self.data = data
if mins is None:
mins = data[["latitude", "longitude"]].to_numpy().min(0)
if maxs is None:
maxs = data[["latitude", "longitude"]].to_numpy().max(0)
self.mins = np.asarray(mins)
self.maxs = np.asarray(maxs)
self.sizes = self.maxs - self.mins
self.children = []
mids = 0.5 * (self.mins + self.maxs)
xmin, ymin = self.mins
xmax, ymax = self.maxs
xmid, ymid = mids
if depth > 0 and len(self.data) >= do_split:
# split the data into four quadrants
data_q1 = data[(data["latitude"] < mids[0]) & (data["longitude"] < mids[1])]
data_q2 = data[
(data["latitude"] < mids[0]) & (data["longitude"] >= mids[1])
]
data_q3 = data[
(data["latitude"] >= mids[0]) & (data["longitude"] < mids[1])
]
data_q4 = data[
(data["latitude"] >= mids[0]) & (data["longitude"] >= mids[1])
]
# recursively build a quad tree on each quadrant which has data
if data_q1.shape[0] > min_split:
self.children.append(
QuadTree(data_q1, [xmin, ymin], [xmid, ymid], id + "0", depth - 1)
)
if data_q2.shape[0] > min_split:
self.children.append(
QuadTree(data_q2, [xmin, ymid], [xmid, ymax], id + "1", depth - 1)
)
if data_q3.shape[0] > min_split:
self.children.append(
QuadTree(data_q3, [xmid, ymin], [xmax, ymid], id + "2", depth - 1)
)
if data_q4.shape[0] > min_split:
self.children.append(
QuadTree(data_q4, [xmid, ymid], [xmax, ymax], id + "3", depth - 1)
)
def unwrap(self):
if len(self.children) == 0:
return {self.id: [self.mins, self.maxs, self.data.copy()]}
else:
d = dict()
for child in self.children:
d.update(child.unwrap())
return d
def extract(qt):
cluster = qt.unwrap()
boundaries, data = {}, []
for id, vs in cluster.items():
(min_lat, min_lon), (max_lat, max_lon), points = vs
points["category"] = id
data.append(points)
boundaries[id] = (
float(min_lat),
float(min_lon),
float(max_lat),
float(max_lon),
)
data = pd.concat(data)
return boundaries, data
if __name__ == "__main__":
# merge into one DataFrame
data_path = join(dirname(dirname(__file__)), "datasets", "osv5m")
train_fp = join(data_path, f"train.csv")
test_fp = join(data_path, f"test.csv")
df_train = pd.read_csv(train_fp)
df_train["split"] = "train"
df_test = pd.read_csv(test_fp)
df_test["split"] = "test"
df = pd.concat([df_train, df_test])
size_before = df.shape[0]
qt = QuadTree(df, depth=15)
boundaries, df = extract(qt)
assert df.shape[0] == size_before
location = reverse_geocoder.search(
[(lat, lon) for lat, lon in zip(df["latitude"], df["longitude"])]
)
df["city"] = [l.get("name", "") for l in location]
df["country"] = [l.get("cc", "") for l in location]
del location
df_train = df[df["split"] == "train"].drop(["split"], axis=1)
df_test = df[df["split"] == "test"].drop(["split"], axis=1)
assert (df_train.shape[0] + df_test.shape[0]) == size_before
json.dump(boundaries, open(join(data_path, "borders.json"), "w"))
df_train.to_csv(train_fp, index=False)
df_test.to_csv(test_fp, index=False)
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