anomalies

Calculates the anomaly score using the Isolation Forest modeling (a way of creating a decision tree model by sampling some data).

Syntax

Calculate the anomaly score using a stored training model.

anomalies [sample=INT] [size=INT] model=MODEL

Calculate the anomaly score using a model trained based on subquery results.

anomalies [sample=INT] [size=INT] FIELD, ... [ SUBQUERY ]
Required Parameters
FIELD, ...
Fields to be used for the Isolation Forest modeling.
model=MODEL
Name of the Isolation Forest model. You can generate and train the Isolation Forest model by connecting to the Logpresso engine via CLI.
[ SUBQUERY ]
Subquery that returns the data set for model training.
Optional Parameters
sample=INT
Number of samples to draw when training the Isolation Forest model (default: the square root of the number of samples).
size=INT
Number of trees within the Isolation Forest (default: 100).

Description

The anomaly score, ranging from 0 to 1, is assigned to the _score field.

  • The higher the score, the more likely it is an anomaly.
  • A score much smaller than 0.5 indicates normal observations.
  • If all scores are close to 0.5, the entire sample does not seem to have clearly distinct anomalies.

Usages

  1. Calculate the anomaly score using the anomal_stock model.

    # Download: https://raw.githubusercontent.com/logpresso/dataset/main/stocks.csv
    | table stocks
    | anomalies model=anomal_stock
    | eval anom = if(_score>0.7, stocks, null)
    
  2. Calculate using a model trained based on the training data set returned from a subquery.

    table stocks
    | anomalies sample=256 stocks 
        [ csvfile /test/sam_train.csv
          | eval _time=date(date, "yyyyMMdd"), stocks = int (stocks)
          | fields _time, stocks
        ]
    | eval anom = if(_score>0.65, stocks, null)
    | fields _time, anom, stocks