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https://github.com/linkedin/luminol
Anomaly Detection and Correlation library
https://github.com/linkedin/luminol
anomaly-detection anomalydetection
Last synced: 20 days ago
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Anomaly Detection and Correlation library
- Host: GitHub
- URL: https://github.com/linkedin/luminol
- Owner: linkedin
- License: apache-2.0
- Created: 2015-11-18T23:16:33.000Z (almost 9 years ago)
- Default Branch: master
- Last Pushed: 2023-05-09T00:52:44.000Z (over 1 year ago)
- Last Synced: 2024-10-01T13:43:10.812Z (about 1 month ago)
- Topics: anomaly-detection, anomalydetection
- Language: Python
- Size: 209 KB
- Stars: 1,183
- Watchers: 65
- Forks: 215
- Open Issues: 34
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# luminol
[](https://travis-ci.org/linkedin/luminol)
[](https://travis-ci.org/linkedin/luminol)
### Overview
Luminol is a light weight python library for time series data analysis. The two major functionalities it supports are anomaly detection and correlation. It can be used to investigate possible causes of anomaly. You collect time series data and Luminol can:
* Given a time series, detect if the data contains any anomaly and gives you back a time window where the anomaly happened in, a time stamp where the anomaly reaches its severity, and a score indicating how severe is the anomaly compare to others in the time series.
* Given two time series, help find their correlation coefficient. Since the correlation mechanism allows a shift room, you are able to correlate two peaks that are slightly apart in time.
Luminol is configurable in a sense that you can choose which specific algorithm you want to use for anomaly detection or correlation. In addition, the library does not rely on any predefined threshold on the values of a time series. Instead, it assigns each data point an anomaly score and identifies anomalies using the scores.
By using the library, we can establish a logic flow for root cause analysis. For example, suppose there is a spike in network latency:
* Anomaly detection discovers the spike in network latency time series
* Get the anomaly period of the spike, and correlate with other system metrics(GC, IO, CPU, etc.) in the same time range
* Get a ranked list of correlated metrics, and the root cause candidates are likely to be on the top.
Investigating the possible ways to automate root cause analysis is one of the main reasons we developed this library and it will be a fundamental part of the future work.
***
### Installation
make sure you have python, pip, numpy, and install directly through pip:
```
pip install luminol
```
the most up-to-date version of the library is 0.4.
***
### Quick Start
This is a quick start guide for using luminol for time series analysis.
1. import the library
```python
import luminol
```
2. conduct anomaly detection on a single time series ts.
```python
detector = luminol.anomaly_detector.AnomalyDetector(ts)
anomalies = detector.get_anomalies()
```
3. if there is anomaly, correlate the first anomaly period with a secondary time series ts2.
```python
if anomalies:
time_period = anomalies[0].get_time_window()
correlator = luminol.correlator.Correlator(ts, ts2, time_period)
```
4. print the correlation coefficient
```python
print(correlator.get_correlation_result().coefficient)
```
These are really simple use of luminol. For information about the parameter types, return types and optional parameters, please refer to the API.
***
### Modules
Modules in Luminol refers to customized classes developed for better data representation, which are `Anomaly`, `CorrelationResult` and `TimeSeries`.
#### Anomaly
_class_ luminol.modules.anomaly.**Anomaly**
It contains these attributes:
```python
self.start_timestamp: # epoch seconds represents the start of the anomaly period.
self.end_timestamp: # epoch seconds represents the end of the anomaly period.
self.anomaly_score: # a score indicating how severe is this anomaly.
self.exact_timestamp: # epoch seconds indicates when the anomaly reaches its severity.
```
It has these public methods:
* `get_time_window()`: returns a tuple (start_timestamp, end_timestamp).
#### CorrelationResult
_class_ luminol.modules.correlation_result.**CorrelationResult**
It contains these attributes:
```python
self.coefficient: # correlation coefficient.
self.shift: # the amount of shift needed to get the above coefficient.
self.shifted_coefficient: # a correlation coefficient with shift taken into account.
```
#### TimeSeries
_class_ luminol.modules.time_series.**TimeSeries**
```python
__init__(self, series)
```
* `series(dict)`: timestamp -> value
It has a various handy methods for manipulating time series, including generator `iterkeys`, `itervalues`, and `iteritems`. It also supports binary operations such as add and subtract. Please refer to the [code](https://github.com/linkedin/naarad/blob/master/lib/luminol/src/luminol/modules/time_series.py) and inline comments for more information.
***
### API
The library contains two classes: `AnomalyDetector` and `Correlator`, and there are two sets of APIs, one corresponding to each class. There are also customized modules for better data representation. The [Modules](#modules) section in this documentation may provide useful information as you walk through the APIs.
#### AnomalyDetector
_class_ luminol.anomaly_detector.**AnomalyDetecor**
```python
__init__(self, time_series, baseline_time_series=None, score_only=False, score_threshold=None,
score_percentile_threshold=None, algorithm_name=None, algorithm_params=None,
refine_algorithm_name=None, refine_algorithm_params=None)
```
* `time_series`: The metric you want to conduct anomaly detection on. It can have the following three types:
```python
1. string: # path to a csv file
2. dict: # timestamp -> value
3. lumnol.modules.time_series.TimeSeries
```
* `baseline_time_series`: an optional baseline time series of one the types mentioned above.
* `score only(bool)`: if asserted, anomaly scores for the time series will be available, while anomaly periods will not be identified.
* `score_threshold`: if passed, anomaly scores above this value will be identified as anomaly. It can override score_percentile_threshold.
* `score_precentile_threshold`: if passed, anomaly scores above this percentile will be identified as anomaly. It can not override score_threshold.
* `algorithm_name(string)`: if passed, the specific algorithm will be used to compute anomaly scores.
* `algorithm_params(dict)`: additional parameters for algorithm specified by algorithm_name.
* `refine_algorithm_name(string)`: if passed, the specific algorithm will be used to compute the time stamp of severity within each anomaly period.
* `refine_algorithm_params(dict)`: additional parameters for algorithm specified by refine_algorithm_params.
Available algorithms and their additional parameters are:
```python
1. 'bitmap_detector': # behaves well for huge data sets, and it is the default detector.
{
'precision'(4): # how many sections to categorize values,
'lag_window_size'(2% of the series length): # lagging window size,
'future_window_size'(2% of the series length): # future window size,
'chunk_size'(2): # chunk size.
}
2. 'default_detector': # used when other algorithms fails, not meant to be explicitly used.
3. 'derivative_detector': # meant to be used when abrupt changes of value are of main interest.
{
'smoothing factor'(0.2): # smoothing factor used to compute exponential moving averages
# of derivatives.
}
4. 'exp_avg_detector': # meant to be used when values are in a roughly stationary range.
# and it is the default refine algorithm.
{
'smoothing factor'(0.2): # smoothing factor used to compute exponential moving averages.
'lag_window_size'(20% of the series length): # lagging window size.
'use_lag_window'(False): # if asserted, a lagging window of size lag_window_size will be used.
}
```
It may seem vague for the meanings of some parameters above. Here are some useful insights:
* [Bitmap](http://alumni.cs.ucr.edu/~ratana/SSDBM05.pdf)
* [Exponential Moving Avg](http://en.wikipedia.org/wiki/Exponential_smoothing)
The **AnomalyDetector** class has the following public methods:
* `get_all_scores()`: returns an anomaly score time series of type [TimeSeries](#modules).
* `get_anomalies()`: return a list of [Anomaly](#modules) objects.
#### Correlator
_class_ luminol.correlator.**Correlator**
```python
__init__(self, time_series_a, time_series_b, time_period=None, use_anomaly_score=False,
algorithm_name=None, algorithm_params=None)
```
* `time_series_a`: a time series, for its type, please refer to time_series for AnomalyDetector above.
* `time_series_b`: a time series, for its type, please refer to time_series for AnomalyDetector above.
* `time_period(tuple)`: a time period where to correlate the two time series.
* `use_anomaly_score(bool)`: if asserted, the anomaly scores of the time series will be used to compute correlation coefficient instead of the original data in the time series.
* `algorithm_name`: if passed, the specific algorithm will be used to calculate correlation coefficient.
* `algorithm_params`: any additional parameters for the algorithm specified by algorithm_name.
Available algorithms and their additional parameters are:
```python
1. 'cross_correlator': # when correlate two time series, it tries to shift the series around so that it
# can catch spikes that are slightly apart in time.
{
'max_shift_seconds'(60): # maximal allowed shift room in seconds,
'shift_impact'(0.05): # weight of shift in the shifted coefficient.
}
```
The **Correlator** class has the following public methods:
* `get_correlation_result()`: return a [CorrelationResult](#modules) object.
* `is_correlated(threshold=0.7)`: if coefficient above the passed in threshold, return a [CorrelationResult](#modules) object. Otherwise, return false.
### Example
1. Calculate anomaly scores.
```python
from luminol.anomaly_detector import AnomalyDetector
ts = {0: 0, 1: 0.5, 2: 1, 3: 1, 4: 1, 5: 0, 6: 0, 7: 0, 8: 0}
my_detector = AnomalyDetector(ts)
score = my_detector.get_all_scores()
for timestamp, value in score.iteritems():
print(timestamp, value)
""" Output:
0 0.0
1 0.873128250131
2 1.57163085024
3 2.13633686334
4 1.70906949067
5 2.90541813415
6 1.17154110935
7 0.937232887479
8 0.749786309983
"""
```
2. Correlate ts1 with ts2 on every anomaly.
```python
from luminol.anomaly_detector import AnomalyDetector
from luminol.correlator import Correlator
ts1 = {0: 0, 1: 0.5, 2: 1, 3: 1, 4: 1, 5: 0, 6: 0, 7: 0, 8: 0}
ts2 = {0: 0, 1: 0.5, 2: 1, 3: 0.5, 4: 1, 5: 0, 6: 1, 7: 1, 8: 1}
my_detector = AnomalyDetector(ts1, score_threshold=1.5)
score = my_detector.get_all_scores()
anomalies = my_detector.get_anomalies()
for a in anomalies:
time_period = a.get_time_window()
my_correlator = Correlator(ts1, ts2, time_period)
if my_correlator.is_correlated(threshold=0.8):
print("ts2 correlate with ts1 at time period (%d, %d)" % time_period)
""" Output:
ts2 correlates with ts1 at time period (2, 5)
"""
```
### Contributing
Clone source and install package and dev requirements:
```bash
pip install -r requirements.txt
pip install pytest pytest-cov pylama
```
Tests and linting run with:
```bash
python -m pytest --cov=src/luminol/ src/luminol/tests/
python -m pylama -i E501 src/luminol/
```