Delete prep_labels.py

prep_labels.py

@@ -1,71 +0,0 @@
-import pandas as pd
-import argparse
-
-def process_to_multiclass(file_path):
-    # Load the TSV file into a pandas DataFrame, treating "NA" as a valid string
-    df = pd.read_csv(file_path, sep='\t', keep_default_na=False)
-
-    # Rename the 'label' column to 'register'
-    if 'label' in df.columns:
-        df.rename(columns={'label': 'register'}, inplace=True)
-
-    # Define the allowed register labels
-    allowed_labels = {"IN", "OP", "NA", "IP", "ID", "HI", "LY", "SP", "OTHER"}
-
-    # Create the new 'label' column by filtering the allowed labels from the 'register' column
-    def extract_labels(register_value, index):
-        if pd.isna(register_value) or register_value == "":
-            print(f"Problematic row {index}: Missing 'register' value")
-            return ""  # Return an empty string or handle missing data as desired
-        try:
-            return ' '.join([label for label in register_value.split() if label in allowed_labels])
-        except Exception as e:
-            print(f"Problematic row {index}: {register_value} caused error: {e}")
-            return ""  # Handle invalid data gracefully
-
-    # Apply the extraction function with index tracking for debugging
-    df['label'] = df.apply(lambda row: extract_labels(row['register'], row.name), axis=1)
-
-    # Save the DataFrame back to a TSV file (overwrites the original)
-    output_file = 'output_' + file_path
-    df.to_csv(output_file, sep='\t', index=False)
-    print(f"File processed successfully. Output saved to {output_file}")
-
-def process_to_multilabel(file_path):
-    # Load the TSV file into a pandas DataFrame, treating "NA" as a valid string
-    df = pd.read_csv(file_path, sep='\t', keep_default_na=False)
-
-    # Rename the 'label' column to 'register'
-    if 'label' in df.columns:
-        df.rename(columns={'label': 'register'}, inplace=True)
-
-    # Create the new 'label' column by ws splitting the 'register' column and saving a list
-    def extract_labels(register_value, index):
-        try:
-            return register_value.split()
-        except Exception as e:
-            print(f"Problematic row {index}: {register_value} caused error: {e}")
-            return ""
-
-    # Apply the extraction function with index tracking for debugging
-    df['full_label'] = df.apply(lambda row: extract_labels(row['register'], row.name), axis=1)
-
-    # only keep major labels for the 'label' column
-    allowed_labels = {"IN", "OP", "NA", "IP", "ID", "HI", "LY", "SP", "OTHER"}
-    df['label'] = df['full_label'].apply(lambda x: [label for label in x if label in allowed_labels])
-
-    # Save the DataFrame back to a TSV file 
-    output_file = 'output_' + file_path
-    df.to_csv(output_file, sep='\t', index=False)
-    print(f"File processed successfully. Output saved to {output_file}")
-
-if __name__ == "__main__":
-    # Set up argument parser to take file name from the command line
-    parser = argparse.ArgumentParser(description="Process a TSV file by renaming the label column and filtering register labels.")
-    parser.add_argument('file_path', type=str, help="The path to the input TSV file")
-
-    # Parse the arguments
-    args = parser.parse_args()
-
-    # Call the function with the provided file path
-    process_to_multilabel(args.file_path)