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Constructor kit for machine learning and predictive analytics in Haskell.

Fuml is a Haskell library that presents a uniform interface to four key tasks in machine learning:

  • Classification - predicting binary or discrete outcomes
  • Regression - predicting continuous outcomes
  • Clustering - associate data points with one of many groups
  • Dimensionality reduction - compress many dimensional data points into a smaller number of dimensions

These operations constitute the bread and butter of applied supervised (classification and regression) and unsupervised (clustering and dimensionality reduction) machine learning. By providing a uniform interface to these operations, Fuml greatly facilitates exploring different algorithms for specific datasets. We also provide functionality for feature engineering and assessing model accuracy.

Currently, Fuml only operates on continuous predictors (labels, aka outcomes, may be discrete or continuous or absent). There are functions to help turn discrete predictors into continuous predictors. This may or may not be appropriate for your problem.

Core types

All Fuml learners return a value of type Predict p a, which is a pair of two objects: the internal model state, which has type p and a specific to every learning method, and a prediction function which maps vectors of predictors to a, the type which is the predicted outcome for that learning method.

data Predict p a = Predict
  { model :: p                    -- ^ the internal state of the model
  , predict :: Vector Double -> a -- ^ the prediction function

The central type in Fuml is that of the Supervisor, which can produce Predict values:

                   +-------- monad in which learning runs.
                   | +------ observed outcome type
                   | | 
                   | | +---- internal model state type
                   | | | 
                   | | | +-- predicted outcome type
                   | | | |
                   v v v v                                -}
newtype Supervisor m o p a = Supervisor {
     :: Maybe p                -- previous model state
     -> [(Vector Double, o)]   -- predictors and outcome
     -> m (Predict p a) }      -- new model state and predict function


  • m: the learning method runs in some monad. Often this is the identity monad (m = Identity). If the operation is distributed and requires network interaction, or accessing and updating a cache, m could be IO. If the method requires randomness, for instance as in stochastic gradient descent, m could be RVar.
  • o: the observed outcome for every data type. For instance, for binary classification this could be Bool.
  • p: the internal state of the model, for storing parameters. You may need to access this to interpret the model, but it may heavily depend on the different learning methods. For instance, for linear regression this might be a vector of regression coefficients.
  • a: the predicted outcome. Why are predicted and observed outcomes different? This allows methods to return information about the uncertainty in the prediction. For instance, if your observed outcome is true and false (a classifier; for instance, predict whether individuals are men or women), a prediction of a probability is better (more informative) than a prediction of a true or false value. This allows the method to communicate how confident it is in the prediction.
  • Maybe p: if you have a previous model fit for data that is close, and the method that can benefit from a close solution, you can pass this in here.
  • [(Vector Double, o)]: lists of pairs of predictors and the observed outcome. This argument is likely be more general in future versions of Fuml.

The instantiation of o and a determine what kind of machine learning you're doing. Here are some examples:

Task o a
Classification Bool Bool or Double
Regression Double Double or (Double, Double)
Clustering () Int
Dimensionality reduction () Vector Double

Within each task, there are other possibilities. Classification could be done on any categorical type (if multiclass) and then the outcome might be a vector or map of probabilities of class membership.

Hyper parameters

Some methods have hyper parameters, that is parameters that cannot be learnt directly from the data but determine or parametrises the learning method. We encode this as a function from the hyper parameters to the Supervisor value.

For instance, ridge regression depends on a hyper parameter that penalises the regression coefficients if they deviate from zero (also known as regularisation).

ridge :: Double -> Supervisor Identity Double (Vector Double) Double
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