• Time-series Outlie Detection System
• • Installation
• Examples

Time-series Outlie Detection System

TODS is a full-stack automated machine learning system for outlier detection on multivariate time-series data. TODS provides exahaustive modules for building machine learning-based outlier detection systems including: data processing, time series processing, feature analysis (extraction), detection algorithms, and reinforcement module. The functionalities provided via these modules including: data preprocessing for general purposes, time series data smoothing/transformation, extracting features from time/frequency domains, various detection algorithms, and involving human expertises to calibrate the system. Three common outlier detection scenarios on time-series data can be performed: point-wise detection (time points as outliers), pattern-wise detection (subsequences as outliers), and system-wise detection (sets of time series as outliers), and wide-range of corresponding algorithms are provided in TODS. This package is developed by DATA Lab @ Texas A&M University.

TODS is featured for:

• Full Sack Machine Learning System which supports exhaustive components from preprocessings, feature extraction, detection algorithms and also human-in-the loop interface.

• Wide-range of Algorithms, including all of the point-wise detection algorithms supported by PyOD, state-of-the-art pattern-wise (collective) detection algorithms such as DeepLog, Telemanon, and also various ensemble algorithms for performing system-wise detection.

• Automated Machine Learning aims on providing knowledge-free process that construct optimal pipeline based on the given data by automatically searching the best combination from all of the existing modules.

Installation

This package works with Python 3.6 and pip 19+. You need to have the following packages installed on the system (for Debian/Ubuntu):

sudo apt-get install libssl-dev libcurl4-openssl-dev libyaml-dev build-essential libopenblas-dev libcap-dev ffmpeg

Then execute python setup.py install, the script will then install all of the packges to build up TODS.

Examples

Examples are available in /examples. For basic usage, you can evaluate a pipeline on a given datasets. Here, we provide an example to load our default pipeline and evaluate it on a subset of yahoo dataset.

import pandas as pd

from tods import schemas as schemas_utils
from tods.utils import generate_dataset_problem, evaluate_pipeline

table_path = 'datasets/yahoo_sub_5.csv'
target_index = 6 # what column is the target
#table_path = 'datasets/NAB/realTweets/labeled_Twitter_volume_IBM.csv' # The path of the dataset
time_limit = 30 # How many seconds you wanna search
#metric = 'F1' # F1 on label 1
metric = 'F1_MACRO' # F1 on both label 0 and 1

# Read data and generate dataset and problem
df = pd.read_csv(table_path)
dataset, problem_description = generate_dataset_problem(df, target_index=target_index, metric=metric)

# Load the default pipeline
pipeline = schemas_utils.load_default_pipeline()

# Run the pipeline
pipeline_result = evaluate_pipeline(problem_description, dataset, pipeline)

We also provide AutoML support to help you automatically find a good pipeline for a your data.

import pandas as pd

from axolotl.backend.simple import SimpleRunner

from tods.utils import generate_dataset_problem
from tods.search import BruteForceSearch

# Some information
#table_path = 'datasets/NAB/realTweets/labeled_Twitter_volume_GOOG.csv' # The path of the dataset
#target_index = 2 # what column is the target

table_path = 'datasets/yahoo_sub_5.csv'
target_index = 6 # what column is the target
#table_path = 'datasets/NAB/realTweets/labeled_Twitter_volume_IBM.csv' # The path of the dataset
time_limit = 30 # How many seconds you wanna search
#metric = 'F1' # F1 on label 1
metric = 'F1_MACRO' # F1 on both label 0 and 1

# Read data and generate dataset and problem
df = pd.read_csv(table_path)
dataset, problem_description = generate_dataset_problem(df, target_index=target_index, metric=metric)

# Start backend
backend = SimpleRunner(random_seed=0)

# Start search algorithm
search = BruteForceSearch(problem_description=problem_description, backend=backend)

# Find the best pipeline
best_runtime, best_pipeline_result = search.search_fit(input_data=[dataset], time_limit=time_limit)
best_pipeline = best_runtime.pipeline
best_output = best_pipeline_result.output

# Evaluate the best pipeline
best_scores = search.evaluate(best_pipeline).scores