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https://github.com/brendanhasz/probflow

A Python package for building Bayesian models with TensorFlow or PyTorch
https://github.com/brendanhasz/probflow

bayesian bayesian-inference bayesian-methods bayesian-neural-networks bayesian-statistics data-science machine-learning python pytorch statistics tensorflow

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A Python package for building Bayesian models with TensorFlow or PyTorch

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README 

        
ProbFlow

========



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ProbFlow is a Python package for building probabilistic Bayesian models with `TensorFlow 2.0 `_ or `PyTorch `_, performing stochastic variational inference with those models, and evaluating the models' inferences.  It provides both high-level modules for building Bayesian neural networks, as well as low-level parameters and distributions for constructing custom Bayesian models.



It's very much still a work in progress.



- **Git repository:** http://github.com/brendanhasz/probflow

- **Documentation:** http://probflow.readthedocs.io

- **Bug reports:** http://github.com/brendanhasz/probflow/issues



Getting Started

---------------



**ProbFlow** allows you to quickly and less painfully build, fit, and evaluate custom Bayesian models (or `ready-made `_ ones!) which run on top of either `TensorFlow 2.0 `_ and `TensorFlow Probability `_ or `PyTorch `_.



With ProbFlow, the core building blocks of a Bayesian model are parameters and probability distributions (and, of course, the input data).  Parameters define how the independent variables (the features) predict the probability distribution of the dependent variables (the target).



For example, a simple Bayesian linear regression



.. image:: https://raw.githubusercontent.com/brendanhasz/probflow/master/docs/img/regression_equation.svg?sanitize=true

   :width: 30 %

   :align: center



can be built by creating a ProbFlow Model.  This is just a class which inherits ``pf.Model`` (or ``pf.ContinuousModel`` or ``pf.CategoricalModel`` depending on the target type).  The ``__init__`` method sets up the parameters, and the ``__call__`` method performs a forward pass of the model, returning the predicted probability distribution of the target:



.. code-block:: python



    import probflow as pf

    import tensorflow as tf



    class LinearRegression(pf.ContinuousModel):



        def __init__(self):

            self.weight = pf.Parameter(name='weight')

            self.bias = pf.Parameter(name='bias')

            self.std = pf.ScaleParameter(name='sigma')



        def __call__(self, x):

            return pf.Normal(x*self.weight()+self.bias(), self.std())



    model = LinearRegression()



Then, the model can be fit using stochastic variational inference, in *one line*:



.. code-block:: python



    # x and y are Numpy arrays or pandas DataFrame/Series

    model.fit(x, y)



You can generate predictions for new data:



.. code-block:: pycon



    # x_test is a Numpy array or pandas DataFrame

    >>> model.predict(x_test)

    [0.983]



Compute *probabilistic* predictions for new data, with 95% confidence intervals:



.. code-block:: python



    model.pred_dist_plot(x_test, ci=0.95)



.. image:: https://raw.githubusercontent.com/brendanhasz/probflow/master/docs/img/pred_dist_light.svg?sanitize=true

   :width: 90 %

   :align: center



Evaluate your model's performance using metrics:



.. code-block:: pycon



    >>> model.metric('mse', x_test, y_test)

    0.217



Inspect the posterior distributions of your fit model's parameters, with 95% confidence intervals:



.. code-block:: python



    model.posterior_plot(ci=0.95)



.. image:: https://raw.githubusercontent.com/brendanhasz/probflow/master/docs/img/posteriors_light.svg?sanitize=true

   :width: 90 %

   :align: center



Investigate how well your model is capturing uncertainty by examining how accurate its predictive intervals are:



.. code-block:: pycon



    >>> model.pred_dist_coverage(ci=0.95)

    0.903



and diagnose *where* your model is having problems capturing uncertainty:



.. code-block:: python



    model.coverage_by(ci=0.95)



.. image:: https://raw.githubusercontent.com/brendanhasz/probflow/master/docs/img/coverage_light.svg?sanitize=true

   :width: 90 %

   :align: center



ProbFlow also provides more complex modules, such as those required for building Bayesian neural networks.  Also, you can mix ProbFlow with TensorFlow (or PyTorch!) code.  For example, even a somewhat complex multi-layer Bayesian neural network like this:



.. image:: https://raw.githubusercontent.com/brendanhasz/probflow/master/docs/img/dual_headed_net_light.svg?sanitize=true

   :width: 99 %

   :align: center



Can be built and fit with ProbFlow in only a few lines:



.. code-block:: python



    class DensityNetwork(pf.ContinuousModel):



        def __init__(self, units, head_units):

            self.core = pf.DenseNetwork(units)

            self.mean = pf.DenseNetwork(head_units)

            self.std  = pf.DenseNetwork(head_units)



        def __call__(self, x):

            z = tf.nn.relu(self.core(x))

            return pf.Normal(self.mean(z), tf.exp(self.std(z)))



    # Create the model

    model = DensityNetwork([x.shape[1], 256, 128], [128, 64, 32, 1])



    # Fit it!

    model.fit(x, y)



For convenience, ProbFlow also includes several `pre-built models `_ for standard tasks (such as linear regressions, logistic regressions, and multi-layer dense neural networks).  For example, the above linear regression example could have been done with much less work by using ProbFlow's ready-made LinearRegression model:



.. code-block:: python



    model = pf.LinearRegression(x.shape[1])

    model.fit(x, y)



And a multi-layer Bayesian neural net can be made easily using ProbFlow's ready-made DenseRegression model:



.. code-block:: python



    model = pf.DenseRegression([x.shape[1], 128, 64, 1])

    model.fit(x, y)



Using parameters and distributions as simple building blocks, ProbFlow allows

for the painless creation of more complicated Bayesian models like `generalized

linear models `_,

`deep time-to-event models

`_,

`neural matrix factorization

`_ models, and

`Gaussian mixture models

`_.  You can even

mix `probabilistic and non-probabilistic models

`_!  Take

a look at the `examples `_

and the `user guide `_

for more!



Installation

------------



If you already have your desired backend installed (i.e. Tensorflow/TFP or

PyTorch), then you can just do:



.. code-block:: bash



    pip install probflow



Or, to install both ProbFlow and the CPU version of TensorFlow + TensorFlow

Probability,



.. code-block:: bash



    pip install probflow[tensorflow]



Or, to install ProbFlow and the GPU version of TensorFlow + TensorFlow

Probability,



.. code-block:: bash



    pip install probflow[tensorflow_gpu]



Or, to install ProbFlow and PyTorch,



.. code-block:: bash



    pip install probflow[pytorch]



Support

-------



Post bug reports, feature requests, and tutorial requests in `GitHub issues `_.



Contributing

------------



`Pull requests `_ are totally welcome!  Any contribution would be appreciated, from things as minor as pointing out typos to things as major as writing new applications and distributions.



Why the name, ProbFlow?

-----------------------



Because it's a package for probabilistic modeling, and it was built on TensorFlow.  ¯\\_(ツ)_/¯