Supervised domain-agnostic prediction framework for probabilistic modelling

Overview

skpro

PyPI version Build Status License

A supervised domain-agnostic framework that allows for probabilistic modelling, namely the prediction of probability distributions for individual data points.

The package offers a variety of features and specifically allows for

  • the implementation of probabilistic prediction strategies in the supervised contexts
  • comparison of frequentist and Bayesian prediction methods
  • strategy optimization through hyperparamter tuning and ensemble methods (e.g. bagging)
  • workflow automation

List of developers and contributors

Documentation

The full documentation is available here.

Installation

Installation is easy using Python's package manager

$ pip install skpro

Contributing & Citation

We welcome contributions to the skpro project. Please read our contribution guide.

If you use skpro in a scientific publication, we would appreciate citations.

Comments
  • Distributions as return objects

    Distributions as return objects

    Re-opening the sub-issue opened in #3 and commented upon by @murphyk

    Question: should skpro's predict methods return a vector of distribution objects? For example, using the distributions from scipy.stats which implement methods pdf, cdf, mean, var, etc.

    Pro:

    • this would be using an existing, consolidated, and well-supported interface
    • it might be easier to use
    • it might be easier to understand

    Contra:

    • mixture types are not supported
    • l2 norm is not supported (as would be needed for squared/Gneiting loss)
    • mixed distributions on the reals, especially empirical distributions (weighted sum of deltas) which are returned by Bayesian packages are not supported
    • vectors of distributions are not supported, alternatively Cartesian products of distributions
    • this is not the status quo
    help wanted 
    opened by fkiraly 11
  • documentation: np.mean(y_pred) does not work

    documentation: np.mean(y_pred) does not work

    I'm following along with this intro example.. However this line fails

    (numpy.mean(y_pred) * 2).shape
    

    Error below (seems to be because Distribution objects don't support the mean() function but instead insist on obscurely calling it point!)

    np.mean(y_pred)
    Traceback (most recent call last):
    
      File "<ipython-input-38-19819be87ab5>", line 1, in <module>
        np.mean(y_pred)
    
      File "/home/kpmurphy/anaconda3/lib/python3.7/site-packages/numpy/core/fromnumeric.py", line 2920, in mean
        out=out, **kwargs)
    
      File "/home/kpmurphy/anaconda3/lib/python3.7/site-packages/numpy/core/_methods.py", line 75, in _mean
        ret = umr_sum(arr, axis, dtype, out, keepdims)
    
    TypeError: unsupported operand type(s) for +: 'Distribution' and 'Distribution'
    
    opened by murphyk 3
  • First example: 'utils' not found

    First example: 'utils' not found

    The first example in your documentation (DensityBaseline) does not run right on my machine: it throws a 'module not found' exception at the call to 'utils'.

    This might be a python version problem (I am using 3.6), so perhaps it's not an error in the normal sense - though I don't see any specification that the package required a particular python version. Apologies if I missed it: in any case, I fixed it by importing matplotlib instead: i.e.

    import matplotlib.pyplot as plt plt.scatter(y_test, y_pred)

    instead of:

    import utils utils.plot_performance(y_test, y_pred)

    opened by Thomas-M-H-Hope 2
  • problem in loading the skpro

    problem in loading the skpro

    It has been 2 days that I am trying to import skpro. But I can not I keep getting this error:

    cannot import name 'six' from 'sklearn.externals' (C:\Users\My Book\anaconda3\lib\site-packages\sklearn\externals_init_.py)

    opened by honestee 1
  • (wish)list of probabilistic regressors to implement or to interface

    (wish)list of probabilistic regressors to implement or to interface

    A wishlist for probabilistic regression methods to implement or interface. This is partly copied from the R counterpart https://github.com/mlr-org/mlr3proba/issues/32 . Number of stars at the end is estimated difficulty or time investment.

    GLM

    • [ ] generalized linear model(s) with regression link, e.g., Gaussian *
    • [ ] generalized linear model(s) with count link, e.g., Poisson *
    • [ ] heteroscedastic linear regression ***
    • [ ] Bayesian GLM where conjugate priors are available, e.g., GLM with Gaussian link ***

    KRR aka Gaussian process regression

    • [ ] vanilla kernel ridge regression with fixed kernel parameters and variance *
    • [ ] kernel ridge regression with MLE for kernel parameters and regularization parameter **
    • [ ] heteroscedastic KRR or Gaussian processes ***

    CDE

    • [ ] variants of conditional density estimation (Nadaraya-Watson type) **
    • [ ] reduction to density estimation by binning of input variables, then apply unconditional density estimation **

    Tree-based

    • [ ] probabilistic regression trees **

    Neural networks

    • [ ] interface tensorflow probability - some hard-coded NN architectures **
    • [ ] generic tensorflow probability interface - some hard-coded NN architectures ***

    Bayesian toolboxes

    • [ ] generic pymc3 interface ***
    • [ ] generic pyro interface ****
    • [ ] generic Stan interface ****
    • [ ] generic JAGS interface ****
    • [ ] generic BUGS interface ****
    • [ ] generic Bayesian interface - prior-valued hyperparameters *****

    Pipeline elements for target transformation

    • [ ] distr fixed target transformation **
    • [ ] distr predictive target calibration **

    Composite techniques, reduction to deterministic regression

    • [ ] stick mean, sd, from a deterministic regressor which already has these as return types into some location/scale distr family (Gaussian, Laplace) *
    • [ ] use model 1 for the mean, model 2 fit to residuals (squared, absolute, or log), put this in some location/scale distr family (Gaussian, Laplace) **
    • [ ] upper/lower thresholder for a regression prediction, to use as a pipeline element for a forced lower variance bound **
    • [ ] generic parameter prediction by elicitation, output being plugged into parameters of a distr object not necessarily scale/location ****
    • [ ] reduction via bootstrapped sampling of a determinstic regressor **

    Ensembling type pipeline elements and compositors

    • [ ] simple bagging, averaging of pdf/cdf **
    • [ ] probabilistic boosting ***
    • [ ] probabilistic stacking ***

    baselines

    • [ ] always predict a Gaussian with mean = training mean, var = training var *
    • [ ] IMPORTANT as featureless baseline: reduction to distr/density estimation to produce an unconditional probabilistic regressor **
    • [ ] IMPORTANT as deterministic style baseline: reduction to deterministic regression, mean = prediction by det.regressor, var = training sample var, distr type = Gaussian (or Laplace) **

    Other reduction from/to probabilistic regression

    • [ ] reducing deterministic regression to probabilistic regression - take mean, median or mode **
    • [ ] reduction(s) to quantile regression, use predictive quantiles to make a distr ***
    • [ ] reducing deterministic (quantile) regression to probabilistic regression - take quantile(s) **
    • [ ] reducing interval regression to probabilistic regression - take mean/sd, or take quantile(s) **
    • [ ] reduction to survival, as the sub-case of no censoring **
    • [ ] reduction to classification, by binning ***
    good first issue 
    opened by fkiraly 0
  • skpro-refactoring (version-2)

    skpro-refactoring (version-2)

    See below some comments/description of the coming refactoring contents :

    • Distribution classes refactoring in a more OOD way (see. skpro->distribution)
    • Losse functions (see. metrics->distribution)
    • Estimators (see. metrics->distribution)

    Some descriptive notebooks (in docs->notebooks) and a full set of unit test (in tests) are also available.

    opened by jesellier 24
Releases(v1.0.1-beta)
Owner
The Alan Turing Institute
The UK's national institute for data science and artificial intelligence.
The Alan Turing Institute
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