Create processing_script.py

processing_script.py

@@ -0,0 +1,110 @@
+def download_civilcomments(data_path: str | Path) -> None:
+    print("Downloading CivilComments...")
+    civilcomments_dir = Path(data_path) / "civilcomments"
+    civilcomments_dir.mkdir(parents=True, exist_ok=True)
+    download_and_extract(
+        "https://worksheets.codalab.org/rest/bundles/0x8cd3de0634154aeaad2ee6eb96723c6e/contents/blob/",
+        str(civilcomments_dir / "civilcomments.tar.gz"),
+    )
+
+
+def process_civilcomments(data_path: str | Path, dst: str | Path, keep_raw: bool = False) -> None:
+    print("Processing CivilComments...")
+    df = pd.read_csv(Path(data_path) / "civilcomments/all_data_with_identities.csv", index_col=0)
+    if keep_raw:
+        ds = Dataset.from_pandas(df, preserve_index=False)
+        
+        # save locally
+        ds.save_to_disk(str(Path(dst) / "civilcomments-wilds" / "raw"))
+        
+        # push to hub
+        ds.push_to_hub("<repo name>", "raw")
+
+    # extract labels, features, and metadata
+    input_output_vars = ["id", "split", "comment_text", "toxicity"]
+    identity_vars = ["male", "female", "LGBTQ", "christian", "muslim", "other_religions", "black", "white"]
+    auxiliary_vars = [
+        "identity_any",
+        "severe_toxicity",
+        "obscene",
+        "threat",
+        "insult",
+        "identity_attack",
+        "sexual_explicit",
+    ]
+
+    # remove instances where label or text is missing
+    df = df.loc[df[input_output_vars[-2:]].isna().sum(1) == 0]
+
+    # remove instances where label < 0
+    df = df.loc[df[input_output_vars[-1]] >= 0]
+
+    # keep only columns we need
+    df = df.loc[:, input_output_vars + identity_vars + auxiliary_vars]
+
+    # encode label and identity attributes
+    cols = [input_output_vars[-1]] + identity_vars + auxiliary_vars
+    df[cols] = (df[cols] >= 0.5).astype(int)
+
+    # fmt: off
+    # deduplicate
+    gdf = df.groupby("comment_text")[identity_vars + ["split", "toxicity"]].agg("nunique")
+    gdf["multiple"] = (gdf != 1).sum(1)
+
+    print(f"""
+    There are {df["comment_text"].duplicated().sum()} exact duplicates (i.e., same `comment_text`). 
+    Of these, only {len(gdf.query("multiple > 0"))} are unique `comment_text`.
+
+    Some duplicates appear with different attributes and labels, and some even in multiple splits. 
+    In particular,
+
+    {(gdf[identity_vars + ["split", "toxicity"]] > 1).sum()}
+    """)
+
+    # if duplicates it keeps:
+    #   - the occurrence in the validation set, or
+    #   - the one with higher toxicity, or
+    #   - the one with higher identity_vars (in order they appears in the list)
+    #   - the one with higher auxiliary_vars (in order they appears in the list)
+    print(f"Length before deduplication: {len(df)}")
+    df = (
+        df.sort_values(["comment_text", "split", "toxicity"] + identity_vars + auxiliary_vars, ascending=False)
+        .drop_duplicates(subset="comment_text", keep="first")
+    )
+    print(f"Length after deduplication: {len(df)}")
+
+    # add column with all identity attributes
+    df = (
+        df.assign(active_attributes=lambda _df: _df[identity_vars].values.tolist())
+        .assign(
+            active_attributes=lambda _df: _df["active_attributes"].map(
+                lambda lst: [name for idx, name in zip(lst, identity_vars, strict=True) if idx == 1]
+            )
+        )
+    )
+    # fmt: on
+
+    # add column to flag whether any active attribute is present
+    assert ((df[identity_vars].sum(1) != 0) == (df["active_attributes"].map(len) > 0)).all()  # simple check
+    df["has_active_attrs"] = df[identity_vars].sum(1) != 0
+
+    # add unique identifier as first column
+    df["uid"] = list(range(len(df)))
+
+    # reorder columns nicely
+    cols = df.columns.tolist()
+    df = df[["uid"] + input_output_vars + ["has_active_attrs", "active_attributes"] + identity_vars + auxiliary_vars]
+
+    # convert to DatasetDict
+    ds_dict = {}
+    for split in df["split"].unique().tolist():
+        ds = Dataset.from_pandas(df.query(f"split == '{split}'").drop(columns=["split"]), preserve_index=False)
+        ds = ds.cast_column("toxicity", ClassLabel(num_classes=2, names=["non-toxic", "toxic"]))
+        ds_dict[split if split != "val" else "validation"] = ds
+    ds_dict = DatasetDict(ds_dict)
+
+    # save locally
+    ds_dict.save_to_disk(str(Path(dst) / "civilcomments-wilds" / "texts"))
+    
+    # push to hub
+    ds_dict.push_to_hub("<repo name>", "default")