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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!")
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