fluent_data 1.0.3

A low footprint streaming data modelization library and service.
Documentation

Fluent Data

A low footprint streaming data modelization library and service.

The algorithm reads data points from an input stream, fits the model and sends the updated model to an output stream.

The online algorithm fits a mixed Gaussian model. Components covariances are supposed to be zero, i.e. for a given component all dimensions are independant from each other. This is a very strong hypothesis, thus the algorithm is not suited to all kind of data.

Install and run the program

Install :

cargo install fluent_data

Run the program and enter data points in the standard input. The program will answer with a model:

fluent_data
[5,-1]
[{"mu":[5.0,-1.0],"sigma":null,"weight":0.0}]
[1,1]
[{"mu":[1.0,1.0],"sigma":20.0,"weight":1.0}]
[15,-13]
[{"mu":[1.0,1.0],"sigma":20.0,"weight":0.95},{"mu":[18.5,-16.5],"sigma":15.68,"weight":1.0}]
[11,23]
[{"mu":[1.0,1.0],"sigma":20.0,"weight":0.9025},{"mu":[18.5,-16.5],"sigma":15.68,"weight":0.95},{"mu":[13.5,28.5],"sigma":23.36,"weight":1.0}]
[31,-3]    
[{"mu":[1.0,1.0],"sigma":20.0,"weight":0.857375},{"mu":[18.5,-16.5],"sigma":15.68,"weight":0.9025},{"mu":[13.5,28.5],"sigma":23.36,"weight":0.95},{"mu":[34.125,0.375],"sigma":13.54,"weight":1.0}]
[10,-9]    
[{"mu":[1.0,1.0],"sigma":20.0,"weight":0.81450625},{"mu":[14.032194480946124,-12.557818659658345],"sigma":74.98091984231274,"weight":1.9025},{"mu":[13.5,28.5],"sigma":23.36,"weight":0.9025},{"mu":[34.125,0.375],"sigma":13.54,"weight":0.95}]
[6,-4]
[{"mu":[1.0,1.0],"sigma":20.0,"weight":0.7737809375},{"mu":[11.264857881136951,-9.609388458225668],"sigma":96.60761701682615,"weight":2.9025},{"mu":[13.5,28.5],"sigma":23.36,"weight":0.857375},{"mu":[34.125,0.375],"sigma":13.54,"weight":0.9025}]
[-2,-5]
[{"mu":[6.7297134962820016,-6.8681649994430005],"sigma":241.4742325873156,"weight":4.6762809375},{"mu":[13.5,28.5],"sigma":23.36,"weight":0.81450625},{"mu":[34.125,0.375],"sigma":13.54,"weight":0.857375}]

A model is represented as a json array with an object for each component:

  • mu is the mean of the component,
  • sigma is the variance of the component,
  • weight is the weight of the component (the probability is obtained by deviding the weight by the sum of weights).

Running as a service

The program can be run as a websocket server:

fluent_data --service

Data points must be sent to ws://0.0.0.0:9001/ws/points and model are received from ws://0.0.0.0:9001/ws/models. The port can be customized by setting the PORT environment variable.

For sending and receiving points, the websocket client (websocat)[https://crates.io/crates/websocat] can be used. Open a first terminal that will listen for models:

websocat ws://127.0.0.1:9001/ws/models

Open a second terminal and enter some points:

websocat ws://127.0.0.1:9001/ws/points
[5,-1]
[1,1]
[15,-13]
[11,23]
[31,-3]    
[10,-9]    
[6,-4]
[-2,-5]

The first terminal should display models:

[{"mu":[5.0,-1.0],"sigma":null,"weight":0.0}]
[{"mu":[1.0,1.0],"sigma":20.0,"weight":1.0}]
[{"mu":[1.0,1.0],"sigma":20.0,"weight":0.95},{"mu":[18.5,-16.5],"sigma":15.68,"weight":1.0}]
[{"mu":[1.0,1.0],"sigma":20.0,"weight":0.9025},{"mu":[18.5,-16.5],"sigma":15.68,"weight":0.95},{"mu":[13.5,28.5],"sigma":23.36,"weight":1.0}]
[{"mu":[1.0,1.0],"sigma":20.0,"weight":0.857375},{"mu":[18.5,-16.5],"sigma":15.68,"weight":0.9025},{"mu":[13.5,28.5],"sigma":23.36,"weight":0.95},{"mu":[34.125,0.375],"sigma":13.54,"weight":1.0}]
[{"mu":[1.0,1.0],"sigma":20.0,"weight":0.81450625},{"mu":[14.032194480946124,-12.557818659658345],"sigma":74.98091984231274,"weight":1.9025},{"mu":[13.5,28.5],"sigma":23.36,"weight":0.9025},{"mu":[34.125,0.375],"sigma":13.54,"weight":0.95}]
[{"mu":[1.0,1.0],"sigma":20.0,"weight":0.7737809375},{"mu":[11.264857881136951,-9.609388458225668],"sigma":96.60761701682615,"weight":2.9025},{"mu":[13.5,28.5],"sigma":23.36,"weight":0.857375},{"mu":[34.125,0.375],"sigma":13.54,"weight":0.9025}]
[{"mu":[6.7297134962820016,-6.8681649994430005],"sigma":241.4742325873156,"weight":4.6762809375},{"mu":[13.5,28.5],"sigma":23.36,"weight":0.81450625},{"mu":[34.125,0.375],"sigma":13.54,"weight":0.857375}]

Using the library

See the crate documentation.

Customizing the algorithm

See the customization section of the crate documentation.

How it works

Given a mixed model fitted from data points received so far, whe the new point P arrives:

  • (I) If the distance to its most probable component of mean C is less than 4 times its standard deviation,
    • the new point belongs to this component: the component standard deviation and mean are updated incrementally, its weight is increased by 1;
    • otherwise a new component is created:
      • the standard deviation is set to 1/5 of the distance to the closest component,
      • the mean M is set to a distance of the closest mean equal to 6 times the new standard deviation (CP = 6/5 CM),
      • the weight of the component is set to 1.
  • (II) In the first above case, the two the most probable components of the incoming point are merged if their distance is less than the sum of their standard deviation:
    • the standard deviation is the weighted average of their standard deviation plus the distance between them,
    • the mean is the weighted center of the means.
    • the weight is the sum of the weights.
  • (III) The weight of all components but the one which the ne point belongs to are decayed with a factor of 0.95.
    • All components which weight are lower than 1/100 are removed.

About the implementation

The model is represented in memory with a graph which vertices are model component. Edges links the 2 nearest neighbors of each components (i.e. the two components that could be most probably the same).

The graph is maintained in memory:

  • When a new component is created, its two most probable components become the new vertice neighbors.
  • When the incoming point is merged the component neighborhood is recomputed using the second most probable component of the incoming point which can be interspersed in the current neighborhood.