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"""Create blinded publishable datasets from raw and temporary inputs.
Inputs:
- temporary/all_email_blasts_consolidated.csv
- temporary/sampled_email_addresses.csv
- temporary/sampled_courses.csv
- raw_data/expirations.csv
- raw_data/orders.csv
- .env with LARGE_BLAST_SIZE
Outputs:
- published_data/email_blasts.parquet
- published_data/expirations.parquet
- published_data/orders.parquet
Blinded indexes are the 0-based row positions from the sampled CSV files,
excluding the header row.
"""
from __future__ import annotations
import argparse
import csv
from decimal import Decimal, InvalidOperation
from pathlib import Path
import pyarrow as pa
import pyarrow.parquet as pq
def parse_args() -> argparse.Namespace:
project_root = Path(__file__).resolve().parent.parent
parser = argparse.ArgumentParser(
description="Create publishable blinded datasets from sampled inputs."
)
parser.add_argument(
"--blast-file",
default=str(project_root / "temporary" / "all_email_blasts_consolidated.csv"),
)
parser.add_argument(
"--sampled-emails-file",
default=str(project_root / "temporary" / "sampled_email_addresses.csv"),
)
parser.add_argument(
"--sampled-courses-file",
default=str(project_root / "temporary" / "sampled_courses.csv"),
)
parser.add_argument(
"--expirations-file",
default=str(project_root / "raw_data" / "expirations.csv"),
)
parser.add_argument(
"--orders-file",
default=str(project_root / "raw_data" / "orders.csv"),
)
parser.add_argument(
"--env-file",
default=str(project_root / ".env"),
)
parser.add_argument(
"--output-folder",
default=str(project_root / "published_data"),
)
return parser.parse_args()
def read_env_values(env_path: Path) -> dict[str, str]:
if not env_path.exists():
raise FileNotFoundError(f"Required env file not found: {env_path}")
values: dict[str, str] = {}
with env_path.open("r", encoding="utf-8") as f:
for line in f:
stripped = line.strip()
if not stripped or stripped.startswith("#") or "=" not in stripped:
continue
key, value = stripped.split("=", 1)
values[key.strip()] = value.strip().strip('"').strip("'")
return values
def read_required_large_blast_size(env_path: Path) -> int:
values = read_env_values(env_path)
raw = values.get("LARGE_BLAST_SIZE", "")
if not raw:
raise RuntimeError(f"LARGE_BLAST_SIZE was not found or empty in: {env_path}")
try:
return int(raw)
except ValueError as exc:
raise RuntimeError(
f"LARGE_BLAST_SIZE must be an integer in: {env_path}"
) from exc
def read_index_map(path: Path, required_header: str) -> dict[str, int]:
if not path.exists():
raise FileNotFoundError(f"Required sampled file not found: {path}")
mapping: dict[str, int] = {}
with path.open("r", encoding="utf-8", newline="") as f:
reader = csv.DictReader(f)
if not reader.fieldnames:
raise RuntimeError(f"Sampled file has no header: {path}")
if required_header not in reader.fieldnames:
raise RuntimeError(
f"Sampled file missing required '{required_header}' column: {path}"
)
for index, row in enumerate(reader):
key = (row.get(required_header) or "").strip().lower()
if not key:
raise RuntimeError(
f"Blank {required_header} value found at sampled index {index} in: {path}"
)
if key in mapping:
raise RuntimeError(
f"Duplicate {required_header} value found in sampled file: {key}"
)
mapping[key] = index
if not mapping:
raise RuntimeError(f"No sampled values found in: {path}")
return mapping
def read_blast_exp_columns(blast_path: Path) -> list[str]:
if not blast_path.exists():
raise FileNotFoundError(f"Required blast file not found: {blast_path}")
with blast_path.open("r", encoding="utf-8", newline="") as f:
reader = csv.DictReader(f)
if not reader.fieldnames:
raise RuntimeError(f"Blast file has no header: {blast_path}")
return [
col for col in reader.fieldnames if col and col.strip().lower().endswith("_exp")
]
def get_blast_course_columns(
exp_columns: list[str], course_index_map: dict[str, int]
) -> list[tuple[int, str, str]]:
blinded_columns: list[tuple[int, str, str]] = []
for exp_col in exp_columns:
course_name = exp_col.strip().lower().removesuffix("_exp")
if course_name not in course_index_map:
raise RuntimeError(
f"Course from blast column not found in sampled courses: {course_name}"
)
course_index = course_index_map[course_name]
blinded_columns.append(
(course_index, exp_col, f"blinded_course_{course_index}_exp")
)
blinded_columns.sort(key=lambda item: (item[0], item[2]))
return blinded_columns
def parse_decimal(value: str) -> Decimal:
try:
return Decimal(value)
except InvalidOperation as exc:
raise RuntimeError(f"Invalid decimal value: {value}") from exc
def write_parquet_rows(rows: list[dict[str, object]], fieldnames: list[str], output_path: Path) -> None:
columns = {name: [row.get(name) for row in rows] for name in fieldnames}
table = pa.table(columns)
pq.write_table(table, output_path)
def create_published_blasts(
blast_path: Path,
output_path: Path,
email_index_map: dict[str, int],
large_blast_size: int,
blast_course_columns: list[tuple[int, str, str]],
) -> int:
blinded_exp_columns = [target_col for _, _, target_col in blast_course_columns]
fieldnames = [
"sent_at",
"is_large_blast",
"email_blinded_index",
*blinded_exp_columns,
]
rows: list[dict[str, object]] = []
with blast_path.open("r", encoding="utf-8", newline="") as src:
reader = csv.DictReader(src)
for row in reader:
email = (row.get("email") or "").strip().lower()
if email not in email_index_map:
continue
sent_at = (row.get("sent_at") or "").strip()
if not sent_at:
raise RuntimeError("Blast row missing required sent_at value")
total_recipients_raw = (row.get("total_recipients_of_batch") or "").strip()
try:
total_recipients = int(total_recipients_raw)
except ValueError as exc:
raise RuntimeError(
f"Invalid total_recipients_of_batch value: {total_recipients_raw}"
) from exc
out_row = {
"sent_at": sent_at,
"is_large_blast": 1 if total_recipients >= large_blast_size else 0,
"email_blinded_index": email_index_map[email],
}
for _, source_col, target_col in blast_course_columns:
out_row[target_col] = (row.get(source_col) or "").strip()
rows.append(out_row)
rows.sort(
key=lambda row: (
str(row["sent_at"]),
int(row["is_large_blast"]),
int(row["email_blinded_index"]),
*[str(row[col]) for col in blinded_exp_columns],
)
)
write_parquet_rows(rows, fieldnames, output_path)
return len(rows)
def create_published_expirations(
expirations_path: Path,
output_path: Path,
email_index_map: dict[str, int],
course_index_map: dict[str, int],
) -> int:
fieldnames = [
"email_blinded_index",
"expired_date",
"course_blinded_index",
"our_course",
]
rows: list[dict[str, object]] = []
with expirations_path.open("r", encoding="utf-8", newline="") as src:
reader = csv.DictReader(src)
for row in reader:
email = (row.get("email") or "").strip().lower()
if email not in email_index_map:
continue
certification = (row.get("certification") or "").strip().lower()
if certification not in course_index_map:
raise RuntimeError(
f"Certification not found in sampled courses: {certification}"
)
rows.append(
{
"email_blinded_index": email_index_map[email],
"expired_date": (row.get("expired_date") or "").strip(),
"course_blinded_index": course_index_map[certification],
"our_course": (row.get("our_course") or "").strip(),
}
)
rows.sort(
key=lambda row: (
int(row["email_blinded_index"]),
str(row["expired_date"]),
int(row["course_blinded_index"]),
str(row["our_course"]),
)
)
write_parquet_rows(rows, fieldnames, output_path)
return len(rows)
def create_published_orders(
orders_path: Path,
output_path: Path,
email_index_map: dict[str, int],
) -> int:
fieldnames = ["created_at", "email_blinded_index", "price"]
rows: list[dict[str, object]] = []
with orders_path.open("r", encoding="utf-8", newline="") as src:
reader = csv.DictReader(src)
for row in reader:
email = (row.get("email") or "").strip().lower()
if email not in email_index_map:
continue
rows.append(
{
"created_at": (row.get("created_at") or "").strip(),
"email_blinded_index": email_index_map[email],
"price": (row.get("price") or "").strip(),
}
)
rows.sort(
key=lambda row: (
str(row["created_at"]),
int(row["email_blinded_index"]),
parse_decimal(str(row["price"])),
)
)
write_parquet_rows(rows, fieldnames, output_path)
return len(rows)
def main() -> None:
args = parse_args()
blast_path = Path(args.blast_file)
sampled_emails_path = Path(args.sampled_emails_file)
sampled_courses_path = Path(args.sampled_courses_file)
expirations_path = Path(args.expirations_file)
orders_path = Path(args.orders_file)
env_path = Path(args.env_file)
output_folder = Path(args.output_folder)
output_folder.mkdir(parents=True, exist_ok=True)
large_blast_size = read_required_large_blast_size(env_path)
email_index_map = read_index_map(sampled_emails_path, "email")
course_index_map = read_index_map(sampled_courses_path, "course")
exp_columns = read_blast_exp_columns(blast_path)
blast_course_columns = get_blast_course_columns(exp_columns, course_index_map)
blast_rows = create_published_blasts(
blast_path,
output_folder / "email_blasts.parquet",
email_index_map,
large_blast_size,
blast_course_columns,
)
expiration_rows = create_published_expirations(
expirations_path,
output_folder / "expirations.parquet",
email_index_map,
course_index_map,
)
order_rows = create_published_orders(
orders_path,
output_folder / "orders.parquet",
email_index_map,
)
print(f"Published blast rows: {blast_rows:,}")
print(f"Published expiration rows: {expiration_rows:,}")
print(f"Published order rows: {order_rows:,}")
print(f"Output folder: {output_folder}")
if __name__ == "__main__":
main() |