|
- import sys
- import argparse
- import os
- import pandas as pd
-
- 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, '../../datasets/anomaly/raw_data/yahoo_sub_5.csv')
-
- parser = argparse.ArgumentParser(description='Arguments for running predefined pipelin.')
- parser.add_argument('--table_path', type=str, default=default_data_path,
- help='Input the path of the input data table')
- parser.add_argument('--target_index', type=int, default=6,
- 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, './example_pipelines/autoencoder_pipeline.json'),
- help='Input the path of the pre-built pipeline description')
-
- args = parser.parse_args()
-
- table_path = args.table_path
- target_index = args.target_index # what column is the target
- pipeline_path = args.pipeline_path
- metric = args.metric # F1 on both label 0 and 1
-
- # 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)
- print(pipeline_result.scores)
- #raise pipeline_result.error[0]
|
