Datasets:
File size: 5,348 Bytes
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"""
Convert the Amazon reviews dataset to parquet format.
Usage:
$ make download
$ python convert.py
"""
import os
import gzip
from slugify import slugify
import pandas as pd
OUTPUT_DIR = "amazon_reviews_2013"
CHUNK_SIZE = 2000000
CATEGORIES = {
"Amazon_Instant_Video.txt.gz": "Amazon Instant Video", # 717,651 reviews
"Arts.txt.gz": "Arts", # 27,980 reviews
"Automotive.txt.gz": "Automotive", # 188,728 reviews
"Baby.txt.gz": "Baby", # 184,887 reviews
"Beauty.txt.gz": "Beauty", # 252,056 reviews
"Books.txt.gz": "Book", # 12,886,488 reviews
"Cell_Phones_&_Accessories.txt.gz": "Cell Phone", # 78,930 reviews
"Clothing_&_Accessories.txt.gz": "Clothing", # 581,933 reviews
"Electronics.txt.gz": "Electronics", # 1,241,778 reviews
"Gourmet_Foods.txt.gz": "Gourmet Food", # 154,635 reviews
"Health.txt.gz": "Health", # 428,781 reviews
"Home_&_Kitchen.txt.gz": "Home & Kitchen", # 991,794 reviews
"Industrial_&_Scientific.txt.gz": "Industrial & Scientific", # 137,042 reviews
"Jewelry.txt.gz": "Jewelry", # 58,621 reviews
"Kindle_Store.txt.gz": "Kindle Store", # 160,793 reviews
"Movies_&_TV.txt.gz": "Movie & TV", # 7,850,072 reviews
"Musical_Instruments.txt.gz": "Musical Instrument", # 85,405 reviews
"Music.txt.gz": "Music", # 6,396,350 reviews
"Office_Products.txt.gz": "Office", # 138,084 reviews
"Patio.txt.gz": "Patio", # 206,250 reviews
"Pet_Supplies.txt.gz": "Pet Supply", # 217,170 reviews
"Shoes.txt.gz": "Shoe", # 389,877 reviews
"Software.txt.gz": "Software", # 95,084 reviews
"Sports_&_Outdoors.txt.gz": "Sports & Outdoor", # 510,991 reviews
"Tools_&_Home_Improvement.txt.gz": "Tools & Home Improvement", # 409,499 reviews
"Toys_&_Games.txt.gz": "Toy & Game", # 435,996 reviews
"Video_Games.txt.gz": "Video Game", # 463,669 reviews
"Watches.txt.gz": "Watch", # 68,356 reviews
}
REVIEW_SCORE = {
"1.0": 0,
"2.0": 0,
"4.0": 1,
"5.0": 1,
}
CATEGORIES_LIST = list(CATEGORIES.values())
def to_parquet(categories, output_dir):
"""
Convert a single file to parquet
"""
n_chunks = 0
data = []
for filename in categories:
for entry in parse_file(filename):
if entry:
data.append(entry)
if len(data) == CHUNK_SIZE:
save_parquet(data, n_chunks, output_dir)
data = []
n_chunks += 1
if data:
save_parquet(data, n_chunks, output_dir)
n_chunks += 1
return n_chunks
def save_parquet(data, chunk, output_dir):
"""
Save data to parquet
"""
fname = os.path.join(output_dir, f"complete-{chunk+1:04d}.parquet")
df = pd.DataFrame(data)
# ensure postive and negative reviews are balanced
negative_rows = df[df["review/score"] == 0]
positive_rows = df[df["review/score"] == 1]
min_size = min(len(negative_rows), len(positive_rows))
rows_df = pd.concat([negative_rows.head(min_size), positive_rows.head(min_size)])
rows_df.to_parquet(fname, index=False)
def parse_file(filename):
"""
Parse a single file.
"""
f = gzip.open(filename, "r")
entry = {}
for line in f:
line = line.decode().strip()
colon_pos = line.find(":")
if colon_pos == -1:
entry["product/category"] = CATEGORIES[filename]
if entry["review/score"] == "3.0":
entry = {}
continue
yield clean(entry)
entry = {}
continue
e_name = line[:colon_pos]
rest = line[colon_pos + 2 :]
entry[e_name] = rest
if entry and entry["review/score"] == "3.0":
return
yield clean(entry)
def clean(entry):
"""
Clean the entry
"""
if not entry:
return entry
if entry["product/price"] == "unknown":
entry["product/price"] = None
entry["review/score"] = REVIEW_SCORE[entry["review/score"]]
entry["review/time"] = int(entry["review/time"])
entry["product/category"] = int(CATEGORIES_LIST.index(entry["product/category"]))
numerator, demoninator = entry["review/helpfulness"].split("/")
numerator = int(numerator)
demoninator = int(demoninator)
if demoninator == 0:
entry["review/helpfulness_ratio"] = 0
else:
entry["review/helpfulness_ratio"] = numerator / demoninator
entry["review/helpfulness_total_votes"] = demoninator
# Remove entries
del entry["review/userId"]
del entry["review/profileName"]
del entry["product/productId"]
return entry
def create_directories():
"""
Create all output directories
"""
if not os.path.exists(OUTPUT_DIR):
os.makedirs(OUTPUT_DIR, exist_ok=True)
for category in CATEGORIES.values():
os.makedirs(os.path.join(OUTPUT_DIR, slugify(category)), exist_ok=True)
os.makedirs(os.path.join(OUTPUT_DIR, "all"), exist_ok=True)
def run():
"""
Convert all files to parquet
"""
create_directories()
to_parquet(CATEGORIES, os.path.join(OUTPUT_DIR, "all"))
for path, category in CATEGORIES.items():
to_parquet(
{path: category},
os.path.join(OUTPUT_DIR, slugify(category)),
)
if __name__ == "__main__":
run()
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