Datasets:
File size: 3,678 Bytes
e120dd0 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 |
"""
Convert the Amazon reviews dataset to parquet format.
Usage:
$ make download
$ python convert.py
"""
import os
import gzip
from glob import glob
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
}
def to_parquet():
"""
Convert a single file to parquet
"""
n_chunks = 0
train_data = []
for filename in CATEGORIES:
for entry in parse_file(filename):
train_data.append(entry)
if len(train_data) == CHUNK_SIZE:
save_parquet(train_data, "train", n_chunks)
train_data = []
n_chunks += 1
if train_data:
save_parquet(train_data, "train", n_chunks)
return n_chunks
def save_parquet(data, split, chunk):
"""
Save data to parquet
"""
fname = os.path.join(OUTPUT_DIR, f"{split}-{chunk:04d}-of-nchunks.parquet")
df = pd.DataFrame(data)
df.to_parquet(fname)
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]
yield entry
entry = {}
continue
e_name = line[:colon_pos]
rest = line[colon_pos + 2 :]
entry[e_name] = rest
yield entry
def rename_chunks(n_chunks):
"""
Replace nchunks in filename by the actual number of chunks
"""
for fname in glob(os.path.join(OUTPUT_DIR, "train-*-of-nchunks.parquet")):
new_fname = fname.replace("-nchunks", f"-{n_chunks:04d}")
os.rename(fname, new_fname)
def run():
"""
Convert all files to parquet
"""
if not os.path.exists(OUTPUT_DIR):
os.makedirs(OUTPUT_DIR)
n_chunks = to_parquet()
print(f"{n_chunks} chunks saved")
rename_chunks(n_chunks)
if __name__ == "__main__":
run()
|