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c4ac745 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 | import argparse
import json
import os
import shutil
import pandas as pd
try:
from preprocessor import DataTransformer
from baselines.ClavaDDPM.preprocess_utils import topological_sort
except (ModuleNotFoundError, ImportError):
import importlib
import sys
base_dir = os.path.dirname(__file__)
full_path = os.path.abspath(os.path.join(base_dir, "..", "..", "preprocessor.py"))
spec = importlib.util.spec_from_file_location("preprocessor", full_path)
preprocessor = importlib.util.module_from_spec(spec)
sys.modules["preprocessor"] = preprocessor
spec.loader.exec_module(preprocessor)
DataTransformer = preprocessor.DataTransformer
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser()
subparsers = parser.add_subparsers(dest='op')
pre_parser = subparsers.add_parser('pre')
pre_parser.add_argument("--dataset-dir", "-d", default=os.path.join("data"))
pre_parser.add_argument("--out-dir", "-o", default=os.path.join("."))
post_parser = subparsers.add_parser('desimplify')
post_parser.add_argument("--dataset-dir", "-d", default=os.path.join("data"))
return parser.parse_args()
def main():
args = parse_args()
if args.op == "pre":
table_names = [
"customers", "geolocation", "order_items", "order_payments", "order_reviews",
"orders", "products", "sellers"
]
tables = {
table_name: pd.read_csv(os.path.join(args.dataset_dir, f"olist_{table_name}_dataset.csv"))
for table_name in table_names
}
geolocation = tables["geolocation"].drop(columns=["geolocation_city"])
grouped = geolocation.groupby("geolocation_zip_code_prefix")
state = grouped[["geolocation_state"]].first()
location = grouped[["geolocation_lat", "geolocation_lng"]].mean()
geolocation = pd.concat([state, location], axis=1).reset_index(drop=False)
customers = tables["customers"].drop(columns=["customer_unique_id", "customer_city"])
customers = customers[
customers["customer_zip_code_prefix"].isin(geolocation["geolocation_zip_code_prefix"])
].reset_index(drop=True)
sellers = tables["sellers"].drop(columns=["seller_city"])
sellers = sellers[
sellers["seller_zip_code_prefix"].isin(geolocation["geolocation_zip_code_prefix"])
].reset_index(drop=True)
orders = tables["orders"]
orders = orders[orders["customer_id"].isin(customers["customer_id"])].reset_index(drop=True)
products = tables["products"]
order_items = tables["order_items"]
order_items = order_items[order_items["order_id"].isin(orders["order_id"])]
order_items = order_items[order_items["seller_id"].isin(sellers["seller_id"])].reset_index(drop=True)
order_payments = tables["order_payments"]
order_payments = order_payments[order_payments["order_id"].isin(orders["order_id"])].reset_index(drop=True)
order_payments = order_payments.sort_values(["order_id", "payment_sequential"])
order_reviews = tables["order_reviews"].drop(columns=["review_comment_title", "review_comment_message"])
order_reviews = order_reviews[order_reviews["order_id"].isin(orders["order_id"])].reset_index(drop=True)
order_reviews = order_reviews.groupby("review_id").head(1).reset_index(drop=True)
processors = {
table: DataTransformer() for table in table_names
}
if os.path.exists(os.path.join(args.out_dir, "processor.json")):
with open(os.path.join(args.out_dir, "processor.json"), "r") as f:
loaded = json.load(f)
for table in table_names:
processors[table] = DataTransformer.from_dict(loaded[table])
else:
processors["geolocation"].fit(geolocation, ["geolocation_zip_code_prefix"])
processors["products"].fit(products, ["product_id"])
processors["customers"].fit(customers, ["customer_id"], {
"customer_zip_code_prefix": processors["geolocation"].columns["geolocation_zip_code_prefix"]
})
processors["sellers"].fit(sellers, ["seller_id"], {
"seller_zip_code_prefix": processors["geolocation"].columns["geolocation_zip_code_prefix"],
})
processors["orders"].fit(orders, ["order_id"], {
"customer_id": processors["customers"].columns["customer_id"],
})
processors["order_items"].fit(order_items, ref_cols={
"order_id": processors["orders"].columns["order_id"],
"product_id": processors["products"].columns["product_id"],
"seller_id": processors["sellers"].columns["seller_id"],
})
processors["order_payments"].fit(order_payments, ref_cols={
"order_id": processors["orders"].columns["order_id"],
})
processors["order_reviews"].fit(order_reviews, ref_cols={
"order_id": processors["orders"].columns["order_id"],
})
with open(os.path.join(args.out_dir, "processor.json"), "w") as f:
json.dump({
t: p.to_dict() for t, p in processors.items()
}, f, indent=2)
os.makedirs(args.out_dir, exist_ok=True)
os.makedirs(os.path.join(args.out_dir, "preprocessed"), exist_ok=True)
for table in table_names:
transformed = processors[table].transform(locals()[table])
transformed.to_csv(os.path.join(args.out_dir, f"preprocessed/{table}.csv"), index=False)
shutil.copytree(os.path.join(args.out_dir, "preprocessed"), os.path.join(args.out_dir, "simplified"))
elif args.op == "desimplify":
pass
if __name__ == "__main__":
main()
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