Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/aeternalis-ingenium/anomalytics
The ultimate anomaly detection and its analytics.
https://github.com/aeternalis-ingenium/anomalytics
analysis analytics anomaly-detection data detection engineering inference mathematics numpy open-source pandas prediction python3 research scientific scipy statistics
Last synced: 22 days ago
JSON representation
The ultimate anomaly detection and its analytics.
- Host: GitHub
- URL: https://github.com/aeternalis-ingenium/anomalytics
- Owner: Aeternalis-Ingenium
- License: mit
- Created: 2023-12-02T23:27:09.000Z (11 months ago)
- Default Branch: trunk
- Last Pushed: 2024-10-08T01:24:16.000Z (29 days ago)
- Last Synced: 2024-10-14T15:01:34.920Z (22 days ago)
- Topics: analysis, analytics, anomaly-detection, data, detection, engineering, inference, mathematics, numpy, open-source, pandas, prediction, python3, research, scientific, scipy, statistics
- Language: Python
- Homepage:
- Size: 8.01 MB
- Stars: 4
- Watchers: 2
- Forks: 1
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Codeowners: .github/CODEOWNERS
Awesome Lists containing this project
README
Anomalytics
Your Ultimate Anomaly Detection & Analytics Tool
## Introduction
`anomalytics` is a Python library that aims to implement all statistical methods for the purpose of detecting any sort of anomaly e.g. extreme events, high or low anomalies, etc. This library utilises external dependencies such as:
- [Pandas 2.1.1](https://pandas.pydata.org/)
- [NumPy 1.26.0](https://numpy.org/)
- [SciPy 1.11.3](https://scipy.org/)
- [Matplotlib 3.8.2](https://matplotlib.org/)
- [Pytest-Cov 4.1.0.](https://pytest-cov.readthedocs.io/en/latest/)
- [Black 23.10.0](https://black.readthedocs.io/en/stable/)
- [Isort 5.12.0](https://pycqa.github.io/isort/)
- [MyPy 1.6.1](https://mypy.readthedocs.io/en/stable/)
- [Bandit 1.7.5](https://bandit.readthedocs.io/en/latest/)
`anomalytics` supports the following Python's versions: `3.10.x`, `3.11.x`, `3.12.0`.
## Installation
To use the library, you can install as follow:
```shell
# Install without openpyxl
$ pip3 install anomalytics
# Install with openpyxl
$ pip3 install "anomalytics[extra]"
```
As a contributor/collaborator, you may want to consider installing all external dependencies for development purposes:
```shell
# Install bandit, black, isort, mypy, openpyxl, pre-commit, and pytest-cov
$ pip3 install "anomalytics[codequality,docs,security,testcov,extra]"
```
## Use Case
`anomalytics` can be used to analyze anomalies in your dataset (both as `pandas.DataFrame` or `pandas.Series`). To start, let's follow along with this minimum example where we want to detect extremely high anomalies in our dataset.
Read the walkthrough below, or the concrete examples here:
* [Extreme Anomaly Analysis - DataFrame](https://github.com/Aeternalis-Ingenium/anomalytics/blob/trunk/docs/examples/extreme_anomaly_df_analysis.ipynb)
* [Battery Water Level Analysis - Time Series](https://github.com/Aeternalis-Ingenium/anomalytics/blob/trunk/docs/examples/battery_water_level_analysis.ipynb)
### Anomaly Detection via the `Detector` Instance
1. Import `anomalytics` and initialise our time series of 100_002 rows:
```python
import anomalytics as atics
df = atics.read_ts("./ad_impressions.csv", "csv")
df.head()
```
```shell
datetime xandr gam adobe
0 2023-10-18 09:01:00 52.483571 71.021131 35.681915
1 2023-10-18 09:02:00 49.308678 73.651996 60.347246
2 2023-10-18 09:03:00 53.238443 65.690813 48.120805
3 2023-10-18 09:04:00 57.615149 80.944393 59.550775
4 2023-10-18 09:05:00 48.829233 76.445099 26.710413
```
2. Initialize the needed detector object. Each detector utilises a different statistical method for detecting anomalies. In this example, we'll use POT method and a high anomaly type. Pay attention to the time period that is directly created where the `t2` is 1 by default because "real-time" always targets the "now" period hence 1 (sec, min, hour, day, week, month, etc.):
```python
pot_detector = atics.get_detector(method="POT", dataset=ts, anomaly_type="high")
print(f"T0: {pot_detector.t0}")
print(f"T1: {pot_detector.t1}")
print(f"T2: {pot_detector.t2}")
pot_detector.plot(ptype="line-dataset-df", title=f"Page Impressions Dataset", xlabel="Minute", ylabel="Impressions", alpha=1.0)
```
```shell
T0: 42705
T1: 16425
T2: 6570
```
3. The purpose of using the detector object instead the standalone is to have a simple fix detection flow. In case you want to customize the time window, we can call the `reset_time_window()` to reset `t2` value, even though that will beat the purpose of using a detector object. Pay attention to the period parameters because the method expects a percentage representation of the distribution of period (ranging 0.0 to 1.0):
```python
pot_detector.reset_time_window(
"historical",
t0_pct=0.65,
t1_pct=0.25,
t2_pct=0.1
)
print(f"T0: {pot_detector.t0}")
print(f"T1: {pot_detector.t1}")
print(f"T2: {pot_detector.t2}")
pot_detector.plot(ptype="hist-dataset-df", title="Dataset Distributions", xlabel="Distributions", ylabel="Page Impressions", alpha=1.0, bins=100)
```
```shell
T0: 65001
T1: 25001
T2: 10000
```
4. Now, we can extract exceedances by giving the expected `q`uantile:
```python
pot_detector.get_extremes(0.95)
pot_detector.exeedance_thresholds.head()
```
```shell
xandr gam adobe datetime
0 58.224653 85.177029 60.362306 2023-10-18 09:01:00
1 58.224653 85.177029 60.362306 2023-10-18 09:02:00
2 58.224653 85.177029 60.362306 2023-10-18 09:03:00
3 58.224653 85.177029 60.362306 2023-10-18 09:04:00
4 58.224653 85.177029 60.362306 2023-10-18 09:05:00
```
5. Let's visualize the exceedances and its threshold to have a clearer understanding of our dataset:
```python
pot_detector.plot(ptype="line-exceedance-df", title="Peaks Over Threshold", xlabel="Minute", ylabel="Page Impressions", alpha=1.0)
```
6. Now that we have the exceedances, we can fit our data into the chosen distribution, in this example the "Generalized Pareto Distribution". The first couple rows will be zeroes which is normal because we only fit data that are greater than zero into the wanted distribution:
```python
pot_detector.fit()
pot_detector.fit_result.head()
```
```shell
xandr_anomaly_score gam_anomaly_score adobe_anomaly_score total_anomaly_score datetime
0 1.087147 0.000000 0.000000 1.087147 2023-11-17 00:46:00
1 0.000000 0.000000 0.000000 0.000000 2023-11-17 00:47:00
2 0.000000 0.000000 0.000000 0.000000 2023-11-17 00:48:00
3 0.000000 1.815875 0.000000 1.815875 2023-11-17 00:49:00
4 0.000000 0.000000 0.000000 0.000000 2023-11-17 00:50:00
...
```
7. Let's inspect the GPD distributions to get the intuition of our pareto distribution:
```python
pot_detector.plot(ptype="hist-gpd-df", title="GPD - PDF", xlabel="Page Impressions", ylabel="Density", alpha=1.0, bins=100)
```
8. The parameters are stored inside the detector class:
```python
pot_detector.params
```
```shell
{0: {'xandr': {'c': -0.11675297447288158,
'loc': 0,
'scale': 2.3129766056305603,
'p_value': 0.9198385927065513,
'anomaly_score': 1.0871472537998},
'gam': {'c': 0.0,
'loc': 0.0,
'scale': 0.0,
'p_value': 0.0,
'anomaly_score': 0.0},
'adobe': {'c': 0.0,
'loc': 0.0,
'scale': 0.0,
'p_value': 0.0,
'anomaly_score': 0.0},
'total_anomaly_score': 1.0871472537998},
1: {'xandr': {'c': 0.0,
'loc': 0.0,
'scale': 0.0,
'p_value': 0.0,
'anomaly_score': 0.0},
'gam': {'c': 0.0,
'loc': 0.0,
'scale': 0.0,
'p_value': 0.0,
...
'scale': 0.0,
'p_value': 0.0,
'anomaly_score': 0.0},
'total_anomaly_score': 0.0},
...}
```
9. Last but not least, we can now detect the extremely large (high) anomalies:
```python
pot_detector.detect(0.95)
pot_detector.detection_result
```
```shell
16425 False
16426 False
16427 False
16428 False
16429 False
...
22990 False
22991 False
22992 False
22993 False
22994 False
Name: detected data, Length: 6570, dtype: bool
```
10. Now we can visualize the anomaly scores from the fitting with the anomaly threshold to get the sense of the extremely large values:
```python
pot_detector.plot(ptype="line-anomaly-score-df", title="Anomaly Score", xlabel="Minute", ylabel="Page Impressions", alpha=1.0)
```
11. Now what? Well, while the detection process seems quite straight forward, in most cases getting the details of each anomalous data is quite tidious! That's why `anomalytics` provides a comfortable method to get the summary of the detection so we can see when, in which row, and how the actual anomalous data look like:
```python
pot_detector.detection_summary.head(5)
```
```shell
row xandr gam adobe xandr_anomaly_score gam_anomaly_score adobe_anomaly_score total_anomaly_score anomaly_threshold
2023-11-28 12:06:00 59225 64.117135 76.425925 47.772929 21.445759 0.000000 0.000000 21.445759 19.689885
2023-11-28 12:25:00 59244 40.513415 94.526021 65.921644 0.000000 19.557962 2.685337 22.243299 19.689885
2023-11-28 12:45:00 59264 52.362039 54.191719 79.972860 0.000000 0.000000 72.313273 72.313273 19.689885
2023-11-28 16:48:00 59507 64.753203 70.344142 42.540168 32.543021 0.000000 0.000000 32.543021 19.689885
2023-11-28 16:53:00 59512 35.912221 52.572939 75.621003 0.000000 0.000000 22.199505 22.199505 19.689885
```
12. In every good analysis there is a test! We can evaluate our analysis result with "Kolmogorov Smirnov" 1 sample test to see how far the statistical distance between the observed sample distributions to the theoretical distributions via the fitting parameters (the smaller the `stats_distance` the better!):
```python
pot_detector.evaluate(method="ks")
pot_detector.evaluation_result
```
```shell
column total_nonzero_exceedances stats_distance p_value c loc scale
0 xandr 3311 0.012901 0.635246 -0.128561 0 2.329005
1 gam 3279 0.011006 0.817674 -0.140479 0 3.852574
2 adobe 3298 0.019479 0.161510 -0.133019 0 6.007833
```
13. If 1 test is not enough for evaluation, we can also visually test our analysis result with "Quantile-Quantile Plot" method to observed the sample quantile vs. the theoretical quantile:
```python
# Use the last non-zero parameters
pot_detector.evaluate(method="qq")
# Use a random non-zero parameters
pot_detector.evaluate(method="qq", is_random=True)
```
### Anomaly Detection via Standalone Functions
You have a project that only needs to be fitted? To be detected? Don't worry! `anomalytics` also provides standalone functions as well in case users want to start the anomaly analysis from a different starting points. It is more flexible, but many processing needs to be done by you. LEt's take an example with a different dataset, thistime the water level Time Series!
1. Import `anomalytics` and initialise your time series:
```python
import anomalytics as atics
ts = atics.read_ts(
"water_level.csv",
"csv"
)
ts.head()
```
```shell
2008-11-03 06:00:00 0.219
2008-11-03 07:00:00 -0.041
2008-11-03 08:00:00 -0.282
2008-11-03 09:00:00 -0.368
2008-11-03 10:00:00 -0.400
Name: Water Level, dtype: float64
```
2. Set the time windows of t0, t1, and t2 to compute dynamic expanding period for calculating the threshold via quantile:
```python
t0, t1, t2 = atics.set_time_window(
total_rows=ts.shape[0],
method="POT",
analysis_type="historical",
t0_pct=0.65,
t1_pct=0.25,
t2_pct=0.1
)
print(f"T0: {t0}")
print(f"T1: {t1}")
print(f"T2: {t2}")
```
```shell
T0: 65001
T1: 25001
T2: 10000
```
3. Extract exceedances and indicate that it is a `"high"` anomaly type and what's the `q`uantile:
```python
pot_thresholds = get_threshold_peaks_over_threshold(dataset=ts, t0=t0, "high", q=0.90)
pot_exceedances = atics.get_exceedance_peaks_over_threshold(
dataset=ts,
threshold_dataset=pot_thresholds,
anomaly_type="high"
)
exceedances.head()
```
```shell
2008-11-03 06:00:00 0.859
2008-11-03 07:00:00 0.859
2008-11-03 08:00:00 0.859
2008-11-03 09:00:00 0.859
2008-11-03 10:00:00 0.859
Name: Water Level, dtype: float64
```
4. Compute the anomaly scores for each exceedance and initialize a params for further analysis and evaluation:
```python
params = {}
anomaly_scores = atics.get_anomaly_score(
exceedance_dataset=pot_exceedances,
t0=t0,
gpd_params=params
)
anomaly_scores.head()
```
```shell
2016-04-03 15:00:00 0.0
2016-04-03 16:00:00 0.0
2016-04-03 17:00:00 0.0
2016-04-03 18:00:00 0.0
2016-04-03 19:00:00 0.0
Name: anomaly scores, dtype: float64
...
```
5. Inspect the parameters:
```python
params
```
```shell
{0: {'index': Timestamp('2016-04-03 15:00:00'),
'c': 0.0,
'loc': 0.0,
'scale': 0.0,
'p_value': 0.0,
'anomaly_score': 0.0},
1: {'index': Timestamp('2016-04-03 16:00:00'),
...
'c': 0.0,
'loc': 0.0,
'scale': 0.0,
'p_value': 0.0,
'anomaly_score': 0.0},
...}
```
6. Detect anomalies:
```python
anomaly_threshold = get_anomaly_threshold(
anomaly_score_dataset=anomaly_scores,
t1=t1,
q=0.90
)
detection_result = get_anomaly(
anomaly_score_dataset=anomaly_scores,
threshold=anomaly_threshold,
t1=t1
)
detection_result.head()
```
```shell
2020-03-31 19:00:00 False
2020-03-31 20:00:00 False
2020-03-31 21:00:00 False
2020-03-31 22:00:00 False
2020-03-31 23:00:00 False
Name: anomalies, dtype: bool
```
7. For the test, kolmogorov-smirnov and qq plot are also accessible via standalone functions, but the params need to be processed so it only contains a non-zero parameters since there are no reasons to calculate a zero 😂
```python
nonzero_params = []
for row in range(0, t1 + t2):
if (
params[row]["c"] != 0
or params[row]["loc"] != 0
or params[row]["scale"] != 0
):
nonzero_params.append(params[row])
ks_result = atics.evals.ks_1sample(
dataset=pot_exceedances,
stats_method="POT",
fit_params=nonzero_params
)
ks_result
```
```shell
{'total_nonzero_exceedances': [5028], 'stats_distance': [0.0284] 'p_value': [0.8987], 'c': [0.003566], 'loc': [0], 'scale': [0.140657]}
```
8. Visualize via qq plot:
```python
nonzero_exceedances = exceedances[exceedances.values > 0]
visualize_qq_plot(
dataset=nonzero_exceedances,
stats_method="POT",
fit_params=nonzero_params,
)
```
### Sending Anomaly Notification
We have anomaly you said? Don't worry, `anomalytics` has the implementation to send an alert via E-Mail or Slack. Just ensure that you have your email password or Slack webhook ready. This example shows both application (please read the comments 😎):
1. Initialize the wanted platform:
```python
# Gmail
gmail = atics.get_notification(
platform="email",
sender_address="[email protected]",
password="AIUEA13",
recipient_addresses=["[email protected]", "[email protected]"],
smtp_host="smtp.gmail.com",
smtp_port=876,
)
# Slack
slack = atics.get_notification(
platform="slack",
webhook_url="https://slack.com/my-slack/YOUR/SLACK/WEBHOOK",
)
print(gmail)
print(slack)
```
```shell
'Email Notification'
'Slack Notification'
```
2. Prepare the data for the notification! If you use standalone, you need to process the `detection_result` to become a DataFrame with `row`, ``
```python
# Standalone
detected_anomalies = detection_result[detection_result.values == True]
anomalous_data = ts[detected_anomalies.index]
standalone_detection_summary = pd.DataFrame(
index=anomalous.index.flatten(),
data=dict(
row=[ts.index.get_loc(index) + 1 for index in anomalous.index],
anomalous_data=[data for data in anomalous.values],
anomaly_score=[score for score in anomaly_score[anomalous.index].values],
anomaly_threshold=[anomaly_threshold] * anomalous.shape[0],
)
)
# Detector Instance
detector_detection_summary = pot_detector.detection_summary
```
1. Prepare the notification payload and a custome message if needed:
```python
# Email
gmail.setup(
detection_summary=detection_summary,
message="Extremely large anomaly detected! From Ad Impressions Dataset!"
)
# Slack
slack.setup(
detection_summary=detection_summary,
message="Extremely large anomaly detected! From Ad Impressions Dataset!"
)
```
2. Send your notification! Beware that the scheduling is not implemented since it always depends on the logic of the use case:
```python
# Email
gmail.send
# Slack
slack.send
```
```shell
'Notification sent successfully.'
```
3. Check your email or slack, this example produces the following notification via Slack:
# Reference
* Nakamura, C. (2021, July 13). On Choice of Hyper-parameter in Extreme Value Theory Based on Machine Learning Techniques. arXiv:2107.06074 [cs.LG]. https://doi.org/10.48550/arXiv.2107.06074
* Davis, N., Raina, G., & Jagannathan, K. (2019). LSTM-Based Anomaly Detection: Detection Rules from Extreme Value Theory. In Proceedings of the EPIA Conference on Artificial Intelligence 2019. https://doi.org/10.48550/arXiv.1909.06041
* Arian, H., Poorvasei, H., Sharifi, A., & Zamani, S. (2020, November 13). The Uncertain Shape of Grey Swans: Extreme Value Theory with Uncertain Threshold. arXiv:2011.06693v1 [econ.GN]. https://doi.org/10.48550/arXiv.2011.06693
* Yiannis Kalliantzis. (n.d.). Detect Outliers: Expert Outlier Detection and Insights. Retrieved [23-12-04T15:10:12.000Z], from https://detectoutliers.com/
# Wall of Fame
I am deeply grateful to have met and guided by wonderful people who inspired me to finish my capstone project for my study at CODE university of applied sciences in Berlin (2023). Thank you so much for being you!
* Sabrina Lindenberg
* Adam Roe
* Alessandro Dolci
* Christian Leschinski
* Johanna Kokocinski
* Peter Krauß