topic update

cdp/topic/cities/test.csv

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+size 4631307

cdp/topic/cities/train.csv

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+size 23729214

cdp/topic/cities/val.csv

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+size 4067182

cdp/topic/prepare_cdp_topic.py

@@ -0,0 +1,54 @@
+import os
+import pandas as pd
+import datasets
+
+# Define labels and columns to drop
+labels = ["Adaptation", "Buildings", "Climate Hazards", "Emissions", "Energy", "Food",
+          "Governance and Data Management", "Opportunities", "Strategy", "Transport",
+          "Waste", "Water"]
+
+columns_to_drop = ["id", "Year Reported to CDP", "Organization", "Parent Section",
+                   "Section", "Question Name", "Row Name", "Comments", "Response Answer"]
+
+# Define dataset split paths
+split_paths = {
+    "train": "https://huggingface.co/datasets/iceberg-nlp/climabench/resolve/main/all_data/CDP/Cities/Cities%20Responses/train.csv",
+    "val": "https://huggingface.co/datasets/iceberg-nlp/climabench/resolve/main/all_data/CDP/Cities/Cities%20Responses/val.csv",
+    "test": "https://huggingface.co/datasets/iceberg-nlp/climabench/resolve/main/all_data/CDP/Cities/Cities%20Responses/test.csv"
+}
+
+# Define output directory
+output_dir = "cdp_topic/cities"
+os.makedirs(output_dir, exist_ok=True)  # Create directory if it doesn't exist
+
+
+def label_to_id(example):
+    """Maps the Category/Label to an index based on the labels list."""
+    example["label_index"] = labels.index(example["Label"])
+    return example
+
+
+def prepare_topic_dataset(df: pd.DataFrame) -> pd.DataFrame:
+    """Prepares the dataset by removing unwanted columns and adding label indices."""
+    df = df.drop(columns=columns_to_drop, errors="ignore")  # Remove unnecessary columns
+    df["label_index"] = df["Label"].apply(lambda x: labels.index(x))
+    return df
+
+
+# Process and save each split
+for split, url in split_paths.items():
+    print(f"Processing {split} split...")
+
+    # Load dataset
+    df = pd.read_csv(url)  # Read CSV directly from URL
+
+    # Prepare dataset
+    df = prepare_topic_dataset(df)
+
+    # Save to local CSV
+    save_path = os.path.join(output_dir, f"{split}.csv")
+    df.to_csv(save_path, index=False)
+
+    print(f"Saved: {save_path}")
+
+print("All datasets processed and saved successfully!")