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- 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__))
- #table_path = 'datasets/NAB/realTweets/labeled_Twitter_volume_IBM.csv' # The path of the dataset
-
- parser = argparse.ArgumentParser(description='Arguments for running predefined pipelin.')
- parser.add_argument('--table_path', type=str, default=os.path.join(this_path, '../../datasets/anomaly/system_wise/sample/train.csv'),
- help='Input the path of the input data table')
- parser.add_argument('--system_dir', type=str, default=os.path.join(this_path, '../../datasets/anomaly/system_wise/sample/systems'),
- help='The directory of where the systems are stored')
- parser.add_argument('--target_index', type=int, default=2,
- help='Index of the ground truth (for evaluation)')
- parser.add_argument('--metric',type=str, default='F1_MACRO',
- help='Evaluation Metric (F1, F1_MACRO)')
- parser.add_argument('--pipeline_path', default=os.path.join(this_path, './example_pipelines/system_pipeline.json'),
- help='Input the path of the pre-built pipeline description')
- # parser.add_argument('--pipeline_path', default=os.path.join(this_path, '../tods/resources/default_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
- system_dir = args.system_dir
- 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 ,system_dir)
-
- # Load the default pipeline
- pipeline = load_pipeline(pipeline_path)
-
- # Run the pipeline
- pipeline_result = evaluate_pipeline(dataset, pipeline, metric)
- print(pipeline_result)
-
- # For debugging
- if pipeline_result.status == 'ERRORED':
- raise pipeline_result.error[0]
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