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- import sys
- import argparse
- import os
- import pandas as pd
- from warnings import filterwarnings
- filterwarnings("ignore")
-
- from tods import generate_dataset, load_pipeline, evaluate_pipeline
-
- this_path = os.path.dirname(os.path.abspath(__file__))
- #default_data_path = os.path.join(this_path, './data/web_attack.csv')
- #default_data_path = os.path.join(this_path, './data/swan_sf.csv')
- default_data_path = os.path.join(this_path, './data/shuttle.csv')
-
- parser = argparse.ArgumentParser(description='Arguments for running predefined pipelin.')
- parser.add_argument('--data_path', type=str, default=default_data_path,
- help='Input the path of the input data table')
- parser.add_argument('--target_index', type=int, default=0,
- help='Index of the ground truth (for evaluation)')
- parser.add_argument('--metric', type=str, default='ALL',
- help='Evaluation Metric (F1, F1_MACRO, RECALL, PRECISION, ALL)')
- parser.add_argument('--pipeline_path',
- default=os.path.join(this_path, './pipeline2/autoencoder_pipeline.json'),
- help='Input the path of the pre-built pipeline description')
-
- args = parser.parse_args()
-
- table_path = args.data_path
- target_index = args.target_index # what column is the target
- metric = args.metric # F1 on both label 0 and 1
- pipeline_path = args.pipeline_path
-
- output_name = pipeline_path.split("/")[-1][:-5]
- output_folder = table_path.split("/")[-1].split(".")[0]
-
- # Read data and generate dataset
- df = pd.read_csv(table_path)
- dataset = generate_dataset(df, target_index)
-
- # Load the default pipeline
- pipeline = load_pipeline(pipeline_path)
-
- # Run the pipeline
- pipeline_result = evaluate_pipeline(dataset, pipeline, metric)
- attrs = pipeline_result.__dict__
- print(pipeline_result)
- #print(attrs)
- raise pipeline_result.error[0]
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