first commit

.gitignore

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+*.txt.gz

Makefile

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+download:
+	wget -c "https://snap.stanford.edu/data/amazon/Amazon_Instant_Video.txt.gz"
+	wget -c "https://snap.stanford.edu/data/amazon/Arts.txt.gz"
+	wget -c "https://snap.stanford.edu/data/amazon/Automotive.txt.gz"
+	wget -c "https://snap.stanford.edu/data/amazon/Baby.txt.gz"
+	wget -c "https://snap.stanford.edu/data/amazon/Beauty.txt.gz"
+	wget -c "https://snap.stanford.edu/data/amazon/Books.txt.gz"
+	wget -c "https://snap.stanford.edu/data/amazon/Cell_Phones_&_Accessories.txt.gz"
+	wget -c "https://snap.stanford.edu/data/amazon/Clothing_&_Accessories.txt.gz"
+	wget -c "https://snap.stanford.edu/data/amazon/Electronics.txt.gz"
+	wget -c "https://snap.stanford.edu/data/amazon/Gourmet_Foods.txt.gz"
+	wget -c "https://snap.stanford.edu/data/amazon/Health.txt.gz"
+	wget -c "https://snap.stanford.edu/data/amazon/Home_&_Kitchen.txt.gz"
+	wget -c "https://snap.stanford.edu/data/amazon/Industrial_&_Scientific.txt.gz"
+	wget -c "https://snap.stanford.edu/data/amazon/Jewelry.txt.gz"
+	wget -c "https://snap.stanford.edu/data/amazon/Kindle_Store.txt.gz"
+	wget -c "https://snap.stanford.edu/data/amazon/Movies_&_TV.txt.gz"
+	wget -c "https://snap.stanford.edu/data/amazon/Musical_Instruments.txt.gz"
+	wget -c "https://snap.stanford.edu/data/amazon/Music.txt.gz"
+	wget -c "https://snap.stanford.edu/data/amazon/Office_Products.txt.gz"
+	wget -c "https://snap.stanford.edu/data/amazon/Patio.txt.gz"
+	wget -c "https://snap.stanford.edu/data/amazon/Pet_Supplies.txt.gz"
+	wget -c "https://snap.stanford.edu/data/amazon/Shoes.txt.gz"
+	wget -c "https://snap.stanford.edu/data/amazon/Software.txt.gz"
+	wget -c "https://snap.stanford.edu/data/amazon/Sports_&_Outdoors.txt.gz"
+	wget -c "https://snap.stanford.edu/data/amazon/Tools_&_Home_Improvement.txt.gz"
+	wget -c "https://snap.stanford.edu/data/amazon/Toys_&_Games.txt.gz"
+	wget -c "https://snap.stanford.edu/data/amazon/Video_Games.txt.gz"
+	wget -c "https://snap.stanford.edu/data/amazon/Watches.txt.gz"

amazon_reviews_2013/train-0000-of-0017.parquet

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amazon_reviews_2013/train-0001-of-0017.parquet

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amazon_reviews_2013/train-0009-of-0017.parquet

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amazon_reviews_2013/train-0014-of-0017.parquet

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amazon_reviews_2013/train-0016-of-0017.parquet

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amazon_reviews_2013/train-0017-of-0017.parquet

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convert.py

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+"""
+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()