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split_metadata.py
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split_metadata.py
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import sys
from collections import defaultdict
import json
import numpy as np
import copy
from util import *
def split_and_save_data(vm, target_key_type, method="assign", no_link=False):
# Index metadata by date or camera
vm_dict = defaultdict(list)
for v in vm:
k = to_key(v, target_key_type)
if no_link:
if "url_part" in v: del v["url_part"]
if "url_root" in v: del v["url_root"]
vm_dict[k].append(v)
p = "../data/split/"
check_and_create_dir(p)
print("="*40)
print("="*40)
print("Split data by " + target_key_type)
if method == "random":
vm_train, vm_valid, vm_test = split(vm_dict, target_key_type)
save_json(vm_valid, p+"metadata_validation_random_split_by_"+target_key_type+".json")
save_json(vm_test, p+"metadata_test_random_split_by_"+target_key_type+".json")
save_json(vm_train, p+"metadata_train_random_split_by_"+target_key_type+".json")
elif method == "assign":
if target_key_type == "camera":
three_splits = [
{
"train": ["0-0", "0-3", "0-6", "0-8", "0-12", "0-2", "0-5", "0-9", "0-11", "0-14"],
"valid": ["0-4", "0-10"],
"test": ["1-0", "2-0", "2-1", "2-2", "0-1", "0-7", "0-13"]
}, {
"train": ["0-1", "0-4", "0-7", "0-10", "0-13", "0-0", "0-3", "0-6", "0-8", "0-12"],
"valid": ["0-5", "0-11"],
"test": ["1-0", "2-0", "2-1", "2-2", "0-2", "0-9", "0-14"]
}, {
"train": ["0-2", "0-5", "0-9", "0-11", "0-14", "0-1", "0-4", "0-7", "0-10", "0-13"],
"valid": ["0-3", "0-8"],
"test": ["1-0", "2-0", "2-1", "2-2", "0-0", "0-6", "0-12"]
}, {
"train": ["0-0", "0-1", "0-2", "0-3", "0-5", "0-6", "0-11", "0-12", "0-13", "0-14"],
"valid": ["0-7", "0-9"],
"test": ["1-0", "2-0", "2-1", "2-2", "0-4", "0-8", "0-10"]
}, {
"train": ["0-0", "0-1", "0-2", "0-7", "0-8", "0-9", "0-10", "0-12", "0-13", "0-14"],
"valid": ["0-4", "0-6"],
"test": ["1-0", "2-0", "2-1", "2-2", "0-3", "0-5", "0-11"]
}]
for i in range(len(three_splits)):
print("-"*20)
print("Split %d" % i)
s = three_splits[i]
vm_train, vm_valid, vm_test = split(vm_dict, target_key_type,
train_key=s["train"], valid_key=s["valid"], test_key=s["test"])
save_json(vm_valid, p+"metadata_validation_split_"+str(i)+"_by_"+target_key_type+".json")
save_json(vm_test, p+"metadata_test_split_"+str(i)+"_by_"+target_key_type+".json")
save_json(vm_train, p+"metadata_train_split_"+str(i)+"_by_"+target_key_type+".json")
elif target_key_type == "date":
target_keys = list(vm_dict.keys())
target_keys = sorted(target_keys)[::-1]
train_key, valid_key, test_key = divide_list(target_keys, frac_valid=0.07, frac_test=0.35)
vm_train, vm_valid, vm_test = split(vm_dict, target_key_type,
train_key=train_key, valid_key=valid_key, test_key=test_key)
save_json(vm_valid, p+"metadata_validation_split_by_"+target_key_type+".json")
save_json(vm_test, p+"metadata_test_split_by_"+target_key_type+".json")
save_json(vm_train, p+"metadata_train_split_by_"+target_key_type+".json")
print("The data split is saved in: " + p)
def divide_list(target_keys, frac_valid=0.1, frac_test=0.3):
n_keys = len(target_keys)
n_valid = int(n_keys*frac_valid)
n_test = int(n_keys*frac_test)
test_key = target_keys[:n_test]
valid_key = target_keys[n_test:n_valid+n_test]
train_key = target_keys[n_valid+n_test:]
return (train_key, valid_key, test_key)
def split(vm_dict, target_key_type, train_key=None, valid_key=None, test_key=None):
if train_key is None or valid_key is None or test_key is None:
target_keys = list(vm_dict.keys())
np.random.shuffle(target_keys)
# 10% for validation, 30% for testing
train_key, valid_key, test_key = divide_list(target_keys, frac_valid=0.1, frac_test=0.3)
vm_valid = []
vm_test = []
vm_train = []
for d in vm_dict:
if d in valid_key:
vm_valid += vm_dict[d]
elif d in test_key:
vm_test += vm_dict[d]
elif d in train_key:
vm_train += vm_dict[d]
print("\nTraining:")
print_distribution(vm_train, target_key_type=target_key_type)
print("\nValidation:")
print_distribution(vm_valid, target_key_type=target_key_type)
print("\nTesting:")
print_distribution(vm_test, target_key_type=target_key_type)
n = len(vm_valid) + len(vm_test) + len(vm_train)
print("\nSize of validation set: %d (%.2f)" % (len(vm_valid), len(vm_valid)/n))
print("Size of test set: %d (%.2f)" % (len(vm_test), len(vm_test)/n))
print("Size of training set: %d (%.2f)" % (len(vm_train), len(vm_train)/n))
return (vm_train, vm_valid, vm_test)
def print_distribution(vm, target_key_type):
count_vm = defaultdict(lambda: defaultdict(int))
for v in vm:
k = to_key(v, target_key_type)
label = v["label"]
if label == 1:
count_vm[k]["pos"] += 1
elif label == 0:
count_vm[k]["neg"] += 1
for k in count_vm:
count_vm[k]["sum"] = count_vm[k]["pos"] + count_vm[k]["neg"]
count_vm[k]["pos_%"] = np.round(count_vm[k]["pos"] / count_vm[k]["sum"], 2)
print(json.dumps(count_vm, indent=4))
def to_key(v, target_key_type):
# The file name contains information about camera, date, and bounding box
# We can use this as the key of the dataset for separating training, validation, and test sets
key = v["file_name"].split("-")
if target_key_type == "camera":
return "-".join(key[0:2])
elif target_key_type == "date":
return "-".join(key[2:5])
# Aggregate labels from citizens (label_state) and researchers (label_state_admin)
# "label" means the final aggregated label
# "weight" means the confidence of the aggregated label
def aggregate_label(vm, add_weight=True):
vm = copy.deepcopy(vm)
vm_new = []
for i in range(len(vm)):
has_error = False
v = vm[i]
label_state_admin = v["label_state_admin"]
label_state = v["label_state"]
if label_state_admin == 47: # pos (gold standard)
v["label"] = 1
if add_weight: v["weight"] = 1
print("Warning: found gold standards")
elif label_state_admin == 32: # neg (gold standard)
v["label"] = 0
if add_weight: v["weight"] = 1
print("Warning: found gold standards")
elif label_state_admin == 23: # strong pos
v["label"] = 1
if add_weight:
if label_state == 23: # strong pos
v["weight"] = 1 # (1+1)/2
elif label_state == 16: # strong neg
v["weight"] = 0.5 # (1+0)/2
elif label_state == 20: # weak neg
v["weight"] = 0.66 # (1+0.33)/2
elif label_state == 19: # weak pos
v["weight"] = 0.83 # (1+0.66)/2
else: # not determined by citizens
v["weight"] = 0.75
elif label_state_admin == 16: # strong neg
v["label"] = 0
if add_weight:
if label_state == 23: # strong pos
v["weight"] = 0.5 # (1+0)/2
elif label_state == 16: # strong neg
v["weight"] = 1 # (1+1)/2
elif label_state == 20: # weak neg
v["weight"] = 0.83 # (1+0.66)/2
elif label_state == 19: # weak pos
v["weight"] = 0.66 # (1+0.33)/2
else: # not determined by citizens
v["weight"] = 0.75
else: # not determined by researchers
if label_state == 23: # strong pos
v["label"] = 1
if add_weight: v["weight"] = 1
elif label_state == 16: # strong neg
v["label"] = 0
if add_weight: v["weight"] = 1
elif label_state == 20: # weak neg
v["label"] = 0
if add_weight: v["weight"] = 0.66
elif label_state == 19: # weak pos
v["label"] = 1
if add_weight: v["weight"] = 0.66
else:
has_error = True
if has_error or "label" not in v:
print("Error when aggregating label:")
print(v)
else:
vm_new.append(v)
return vm_new
# Split metadata into training, validation, and test sets
def main(argv):
# Check
if len(argv) > 1:
if argv[1] != "confirm":
print("Must confirm by running: python split_metadata.py confirm")
return
else:
print("Must confirm by running: python split_metadata.py confirm")
return
vm = load_json("../data/metadata.json")
vm = aggregate_label(vm)
method = "assign"
no_link = True
split_and_save_data(vm, "date", method=method, no_link=no_link)
split_and_save_data(vm, "camera", method=method, no_link=no_link)
if __name__ == "__main__":
main(sys.argv)