fix: split train and test set

.vscode/settings.json

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+{
+    "python.analysis.autoFormatStrings": true,
+    "[python]": {
+        "diffEditor.ignoreTrimWhitespace": false,
+        "editor.formatOnType": true,
+        "editor.wordBasedSuggestions": "off",
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+    "files.watcherExclude": {
+        "**/.git/objects/**": true,
+        "**/.git/subtree-cache/**": true,
+    },
+}

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convert.py

@@ -13,8 +13,12 @@ from glob import glob
 
 import pandas as pd
 
+from sklearn.model_selection import train_test_split
+
+
 OUTPUT_DIR = "amazon_reviews_2013"
 CHUNK_SIZE = 2000000
+TEST_SIZE = 0.2
 
 CATEGORIES = {
     "Amazon_Instant_Video.txt.gz": "Amazon Instant Video",  # 717,651 reviews
@@ -47,37 +51,46 @@ CATEGORIES = {
     "Watches.txt.gz": "Watch",  # 68,356 reviews
 }
 
+CATEGORIES_LIST = list(CATEGORIES.values())
+
 
 def to_parquet():
     """
     Convert a single file to parquet
     """
     n_chunks = 0
-    train_data = []
+    data = []
 
     for filename in CATEGORIES:
 
         for entry in parse_file(filename):
-            train_data.append(entry)
+            data.append(entry)
 
-            if len(train_data) == CHUNK_SIZE:
-                save_parquet(train_data, "train", n_chunks)
-                train_data = []
+            if len(data) == CHUNK_SIZE:
+                save_parquet(data, n_chunks)
+                data = []
                 n_chunks += 1
 
-    if train_data:
-        save_parquet(train_data, "train", n_chunks)
+    if data:
+        save_parquet(data, n_chunks)
+        n_chunks += 1
 
     return n_chunks
 
 
-def save_parquet(data, split, chunk):
+def save_parquet(data, chunk):
     """
     Save data to parquet
     """
-    fname = os.path.join(OUTPUT_DIR, f"{split}-{chunk:04d}-of-nchunks.parquet")
+    fname_train = os.path.join(OUTPUT_DIR, f"train-{chunk:04d}-of-nchunks.parquet")
+    fname_test = os.path.join(OUTPUT_DIR, f"test-{chunk:04d}-of-nchunks.parquet")
+
     df = pd.DataFrame(data)
-    df.to_parquet(fname)
+
+    df_train, df_test = train_test_split(df, test_size=TEST_SIZE, random_state=42)
+
+    df_train.to_parquet(fname_train)
+    df_test.to_parquet(fname_test)
 
 
 def parse_file(filename):
@@ -101,11 +114,44 @@ def parse_file(filename):
     yield entry
 
 
+def clean(entry):
+    """
+    Clean the entry
+    """
+
+    if entry["product/price"] == "unknown":
+        entry["product/price"] = None
+    else:
+        entry["product/price"] = float(entry["product/price"])
+
+    entry["review/score"] = int(entry["review/score"])
+    entry["review/time"] = int(entry["review/time"])
+    entry["product/category"] = 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 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")):
+    for fname in glob(os.path.join(OUTPUT_DIR, "*-of-nchunks.parquet")):
         new_fname = fname.replace("-nchunks", f"-{n_chunks:04d}")
         os.rename(fname, new_fname)