anomalytics 0.1.4

The ultimate anomaly detection library.

Anomalytics

Your Ultimate Anomaly Detection & Analytics Tool

Introduction

anomalytics is a Python library that aims to implement all statistical method for the purpose of dtecting any sort of anomaly e.g. extreme events, high or low anomalies, etc. This library utilises external dependencies such as:

• Pandas 2.1.1
• NumPy 1.26.0
• SciPy 1.11.3
• Matplotlib 3.8.2
• Pytest-Cov 4.1.0.
• Black 23.10.0
• Isort 5.12.0
• MyPy 1.6.1
• Bandit 1.7.5

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:

# 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:

# 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 time series dataset.

Anomaly Detection via the Detector Instance

1. Import anomalytics and initialise your time series:

   import anomalytics as atics
   
   ts = atics.read_ts("./my_dataset.csv", "csv")
   ts.head()
   

   Date-Time
   2021-11-03 08:00:00   -0.282
   2021-11-03 09:00:00   -0.368
   2021-11-03 10:00:00   -0.400
   2021-11-03 11:00:00   -0.320
   2021-11-03 12:00:00   -0.155
   Name: Example Dataset, dtype: float64
   

2. Initialize the needed detector object. Each detector utilises a different statistical method to detect the 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.):

   pot_detector = atics.get_detector("POT", ts, "high")
   
   print(f"T0: {pot_detector.t0}")
   print(f"T1: {pot_detector.t1}")
   print(f"T2: {pot_detector.t2}")
   

   T0: 70000
   T1: 19999
   T2: 1
   

3. The purpose of having a detector object is to support dividing the time windows between t0, t1, and t2 because this is a big factor to extract exceedances. In case users want to customize their time window, they can call the reset_time_window() where users can now set t2 greater than 1 as well even though that will beat the purpose of the 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):

   pot_detector.reset_time_window(
       "historical",
       t0_pct=0.7,
       t1_pct=0.2,
       t2_pct=0.1,
   )
   
   print(f"T0: {pot_detector.t0}")
   print(f"T1: {pot_detector.t1}")
   print(f"T2: {pot_detector.t2}")
   

   T0: 70000
   T1: 20000
   T2: 10000
   

4. Let's extract exceedances by giving the expected quantile:

   pot_detector.get_extremes(0.95)
   pot_detector.exeedances.head()
   

   Date-Time
   2022-03-31 19:00:00    0.867
   2022-03-31 20:00:00    0.867
   2022-03-31 21:00:00    0.867
   2022-03-31 22:00:00    0.867
   2022-03-31 23:00:00    0.867
   Name: Example Dataset, dtype: float64
   

5. Let's visualize the exceedances and its threshold to have a clearer understanding of our dataset:

   pot_detector.plot(plot_type="l+eth", title=f"Peaks Over Threshold", xlabel="Daily", ylabel="Water Level", alpha=1.0)
   

   

6. Compute the anomaly score:

   pot_detector.fit()
   pot_detector.fit_result.head()
   

   Date-Time
   2022-10-29 00:00:00    0.0
   2022-10-29 01:00:00    0.0
   2022-10-29 02:00:00    0.0
   2022-10-29 03:00:00    0.0
   2022-10-29 04:00:00    0.0
   Name: Example Dataset, dtype: float64
   ...
   

7. The parameters are now stored inside the detector class:

   pot_detector.params
   

   {0: {'datetime': Timestamp('2022-10-29 03:00:00'),
   'c': 0.0,
   'loc': 0.0,
   'scale': 0.0,
   'p_value': 0.0,
   'anomaly_score': 0.0},
   1: {'datetime': Timestamp('2022-10-29 04:00:00'),
   'c': 0.324456778899,
   'loc': 0,
   'scale': 0.19125308567629334,
   'p_value': 0.19286132173263668,
   'anomaly_score': 5.1850728337654886},
   ...}
   

8. Detect the extremely high anomalies:

   pot_detector.detect(0.90)
   pot_detector.detection_result.head()
   

   Date-Time
   2023-02-09 08:00:00    False
   2023-02-09 09:00:00    False
   2023-02-09 10:00:00    False
   2023-02-09 11:00:00    False
   2023-02-09 12:00:00    False
   Name: Example Dataset, dtype: bool
   

9. Evaluate your analysis result with Kolmogorov Smirnov 1 sample test:

   pot_detector.evaluate("ks")
   pot_detector.evaluation_result.head()
   

       total_nonzero_exceedances	stats_distance	p_value	       c    loc     scale
   0	                     5028	        0.0284	 0.8987 0.003566	  0	 0.140657
   

10. We can also visually test our analysis result with "Quantile-Quantile Plot" method:

    pot_detector.evaluate("qq")
    

    

Anomaly Detection via Standalone Functions

1. Import anomalytics and initialise your time series:

   import anomalytics as atics
   
   ts = atics.read_ts(
       "water_level.csv",
       "csv"
   )
   ts.head()
   

   Date-Time
   2021-11-03 08:00:00   -0.282
   2021-11-03 09:00:00   -0.368
   2021-11-03 10:00:00   -0.400
   2021-11-03 11:00:00   -0.320
   2021-11-03 12:00:00   -0.155
   Name: Example Dataset, dtype: float64
   

2. Set the time windows of t0, t1, and t2 to compute dynamic expanding period for calculating the threshold via quantile:

   t0, t1, t2 = atics.set_time_window(ts.shape[0], "POT", "historical", t0_pct=0.7, t1_pct=0.2, t2_pct=0.1)
   
   print(f"T0: {t0}")
   print(f"T1: {t1}")
   print(f"T2: {t2}")
   

   T0: 70000
   T1: 20000
   T2: 10000
   

3. Extract exceedances and indicate that it is a "high" anomaly type and what's the quantile:

   exceedances = atics.get_exceedance_peaks_over_threshold(ts, ts.shape[0], "high", 0.95)
   exceedances.head()
   

   Date-Time
   2022-03-31 19:00:00    0.867
   2022-03-31 20:00:00    0.867
   2022-03-31 21:00:00    0.867
   2022-03-31 22:00:00    0.867
   2022-03-31 23:00:00    0.867
   Name: Example Dataset, dtype: float64
   

4. Compute the anomaly score for each exceedance and initialize a params for further analysis and evaluation:

   params = {}
   anomaly_scores = atics.get_anomaly_score(exceedance_ts, exceedance_ts.shape[0], params)
   anomaly_scores.head()
   

   Date-Time
   2022-10-29 00:00:00    0.0
   2022-10-29 01:00:00    0.0
   2022-10-29 02:00:00    0.0
   2022-10-29 03:00:00    0.0
   2022-10-29 04:00:00    0.0
   Name: Example Dataset, dtype: float64
   ...
   

5. Inspect the parameters (the result of genpareto fitting):

   params
   

   {0: {'datetime': Timestamp('2022-10-29 03:00:00'),
   'c': 0.0,
   'loc': 0.0,
   'scale': 0.0,
   'p_value': 0.0,
   'anomaly_score': 0.0},
   1: {'datetime': Timestamp('2022-10-29 04:00:00'),
   'c': 0.324456778899,
   'loc': 0,
   'scale': 0.19125308567629334,
   'p_value': 0.19286132173263668,
   'anomaly_score': 5.1850728337654886},
   ...}
   

6. Detect the extremely high anomalies:

   detected_data = atics.detect(anomaly_score_ts, t1, 0.90)
   detected_data.head()
   

   Date-Time
   2023-02-09 08:00:00    False
   2023-02-09 09:00:00    False
   2023-02-09 10:00:00    False
   2023-02-09 11:00:00    False
   2023-02-09 12:00:00    False
   Name: Example Dataset, dtype: bool
   

7. Evaluate your analysis result with Kolmogorov Smirnov 1 sample test:

   ks_result = atics.evals.ks_1sample(ts=exceedance, stats_method="POT", fit_params=params)
   print(ks_result)
   

   {'total_nonzero_exceedances': 5028, 'stats_distance': 0.0284, 'p_value': 0.8987, 'c': 0.003566, 'loc': 0, 'scale': 0.140657}
   

Sending Anomaly Notification

Users can choose to send notification via E-Mail or Slack. These example will show both (please read the comments 😎):

1. Initialize the wanted platform:

   # Gmail
   gmail = atics.get_notification(
       platform="email",
       sender_address="[e-mail]",
       password="AIUEA13",
       recipient_addresses=["[e-mail]", "[e-mail]"],
       smtp_host="smtp.gmail.com",
       smtp_port=876,
   )
   
   # Slack
   slack = atics.get_notification(
       platform="slack",
       webhook_url="https://slack.com/my-slack/1234567890/09876543/213647890",
   )
   
   print(gmail)
   print(slack)
   

   'Email Notification'
   'Slack Notification'
   

2. Prepare the data for the notification. For the standalone analysis, please use exatly the same column names (keys in data)!

   # Standalone
   detected_anomalies = detected_data[detected_data.values == True]
   anomalous_data = ts[detected_anomalies.index]
   data = dict(
       row=[ts.index.get_loc(index) + 1 for index in anomalous.index],
       datetime=[index 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],
   )
   detection_summary = pd.DataFrame(data=data)
   
   
   # Detector Instance
   detection_summary = pot_detector.detection_summary
   
   # Both ways provide the exact same result! Trust me it's tested 😋
   detection_summary.head()
   

           row                 datetime    anomalous_data       anomaly_score  anomaly_threshold
   0       709      2023-11-10 08:00:00           15.1850  5.1850728337654881               5.05
   1       715      2023-11-16 09:00:00           16.7245  6.1850728337654883               5.05
   2       899      2023-11-25 09:00:00           16.9332  6.5245252131234522               5.05
   3       905      2023-11-29 11:00:00           15.2343  5.2952852731624753               5.05
   4       999      2023-11-30 12:00:00           18.2341  8.7246517285291231               5.05
   

3. Prepare the notification payload and a custome message if needed:

   # Email
   gmail.setup(
       detection_summary,
       "Attention, detected anomaly from dataset Water Level:"
   )
   
   # Slack
   slack.setup(
       detection_summary,
       "Attention, detected anomaly from dataset Water Level:"
   )
   

4. Send your notification! Beware that the scheduling is not implemented since it always depends on the logic of the use case:

   # Email
   gmail.send
   
   # Slack
   slack.send
   

5. The above example will produce the following notification:

🤖 Anomalytics - Anomaly Detected!

Attention, detected anomaly from dataset Water Level:

Row: 999 | Date: 2023-11-30 12:00:00 | Anomalous Data: 18.2341 | Anomaly Score: 8.7246517285291231 | Anomaly Threshold: 5.05

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, guided, or even just read some inspirational works from people who motivate me to publish this open-source package as a part of my capstone project at CODE university of applied sciences in Berlin (2023):

• Sabrina Lindenberg
• Adam Roe
• Alessandro Dolci
• Christian Leschinski
• Johanna Kokocinski
• Peter Krauß