Datasets:
File size: 3,451 Bytes
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import pandas as pd
import os
from tqdm import tqdm
import argparse
def main():
"""
Main function to process CSV data and generate input and gt data files for ETT forecasting.
"""
# Set up argument parser
parser = argparse.ArgumentParser(description="Process ETT forecasting data.")
parser.add_argument("--data_path", type=str, required=True, help="Path to the ETTh1.csv file.")
args = parser.parse_args()
try:
# Define the source folder containing the original files
csv_file_path = args.data_path
# Read the CSV file into a DataFrame
df = pd.read_csv(csv_file_path)
# Define target folders for input and ground truth data
target_folder = "Energy-ETT-Transformer_sensor_signal-Forecasting"
input_data_path = os.path.join(target_folder, "raw_input_data")
gt_data_path = os.path.join(target_folder, "raw_gt_data")
# Create directories if they don't exist
os.makedirs(input_data_path, exist_ok=True)
os.makedirs(gt_data_path, exist_ok=True)
# Define sequence and prediction lengths
seq_len_list = [96, 96]
pred_len_list = [96, 720]
label = 0 # No overloap
# Specify the type of data to generate (e.g., "train", "val", "test")
generate_data_type = "test"
# Iterate over sequence and prediction lengths
for seq_len, pred_len in zip(seq_len_list, pred_len_list):
# Define start and end indices for different data types
start_idx = {
"train": 0,
"val": 12 * 30 * 24 - pred_len,
"test": (12 + 4) * 30 * 24 - pred_len,
}
end_idx = {
"train": 12 * 30 * 24 - seq_len - pred_len,
"val": (12 + 4) * 30 * 24 - seq_len - pred_len,
"test": (12 + 8) * 30 * 24 - seq_len - pred_len,
}
# Iterate over the specified range of indices
for i in tqdm(range(start_idx[generate_data_type], end_idx[generate_data_type] + 1), desc=f"Generating data: context_length: {seq_len}, prediction_length: {pred_len}"):
# Extract input and gt data
data_input = df.iloc[i : i + seq_len]
data_gt = df.iloc[i + seq_len : i + seq_len + pred_len]
# Save input and gt data to CSV files, select 'OT' column
data_input[['OT']].to_csv(
os.path.join(input_data_path, f'seq{seq_len}_label{label}_pred{pred_len}_index{i}_input_ts_OT.csv'),
index=False,
header=False,
encoding='utf-8'
)
data_gt[['OT']].to_csv(
os.path.join(gt_data_path, f'seq{seq_len}_label{label}_pred{pred_len}_index{i}_target_ts_OT.csv'),
index=False,
header=False,
encoding='utf-8'
)
except FileNotFoundError:
print(f"Error: File {csv_file_path} not found. Please check the path or filename.")
except pd.errors.EmptyDataError:
print(f"Error: File {csv_file_path} is empty.")
except pd.errors.ParserError:
print(f"Error: File {csv_file_path} is not a valid CSV file.")
except Exception as e:
print(f"An unexpected error occurred: {e}")
if __name__ == "__main__":
main()
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