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InferPy: Deep Probabilistic Modeling with Tensorflow Made Easy

Home Page: https://inferpy-docs.readthedocs.io/en/stable/index.html

License: Apache License 2.0

Python 3.86% Jupyter Notebook 96.13% Shell 0.01%

probabilistic-programming neural-network tensorflow keras-tensorflow probabilistic-modeling user-friendly

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inferpy's Issues

TransformedDistribution

Implement TransformedDistribution
http://edwardlib.org/api/ed/models/TransformedDistribution

type float32 or float64

TODO: standardise the use of tensors of type float32 or float64

ImportError: cannot import name 'Bernoulli'

Hi,

I'm trying to run some of the examples on the documentation page, but it seems like it's not possible to import the Bernoulli model. Has it been removed or am I doing something wrong perhaps?

error with normal of dimension>1

When a multidimensional Normal variable is created, the dimension remains equal to 1, i.e.:

import inferpy as inf

# define a 2-dimension Normal distribution of 2·3=6 batches
with inf.replicate(size=2):
	with inf.replicate(size=3):
		x = inf.models.Normal(loc=0., scale=1., dim=2)


>>> x.shape
[6, 1]

FutureWarning

warning with inference:

...InferPy/env2.7/lib/python2.7/site-packages/edward/util/random_variables.py:52: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
  not np.issubdtype(value.dtype, np.float) and \

latent variables

Allow to define latent variables in the prob models. Now, all the variables are assumed observed.

ValueError with multidimensional parameter distribution

Problem when defining a parameter which is a multidimensional distribution:

theta = inf.models.Normal(loc=[0., 0.], scale=1.)
x = inf.models.Normal(loc=theta, scale=1., observed=True)

raises the error below:

Traceback (most recent call last):
  File "<input>", line 1, in <module>
  File "/Users/rcabanas/Documents/UAL/inferpy/repo/InferPy/inferpy/models/normal.py", line 90, in __init__
    loc_rep = self.__reshape_param(loc, self_shape)
  File "/Users/rcabanas/Documents/UAL/inferpy/repo/InferPy/inferpy/models/normal.py", line 165, in __reshape_param
    param_tf_mat = tf.reshape(tf.stack(param_vect), (D,))
  File "/Users/rcabanas/Documents/UAL/inferpy/repo/InferPy/env2.7/lib/python2.7/site-packages/tensorflow/python/ops/gen_array_ops.py", line 3997, in reshape
    "Reshape", tensor=tensor, shape=shape, name=name)
  File "/Users/rcabanas/Documents/UAL/inferpy/repo/InferPy/env2.7/lib/python2.7/site-packages/tensorflow/python/framework/op_def_library.py", line 787, in _apply_op_helper
    op_def=op_def)
  File "/Users/rcabanas/Documents/UAL/inferpy/repo/InferPy/env2.7/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 3162, in create_op
    compute_device=compute_device)
  File "/Users/rcabanas/Documents/UAL/inferpy/repo/InferPy/env2.7/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 3208, in _create_op_helper
    set_shapes_for_outputs(op)
  File "/Users/rcabanas/Documents/UAL/inferpy/repo/InferPy/env2.7/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 2427, in set_shapes_for_outputs
    return _set_shapes_for_outputs(op)
  File "/Users/rcabanas/Documents/UAL/inferpy/repo/InferPy/env2.7/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 2400, in _set_shapes_for_outputs
    shapes = shape_func(op)
  File "/Users/rcabanas/Documents/UAL/inferpy/repo/InferPy/env2.7/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 2330, in call_with_requiring
    return call_cpp_shape_fn(op, require_shape_fn=True)
  File "/Users/rcabanas/Documents/UAL/inferpy/repo/InferPy/env2.7/lib/python2.7/site-packages/tensorflow/python/framework/common_shapes.py", line 627, in call_cpp_shape_fn
    require_shape_fn)
  File "/Users/rcabanas/Documents/UAL/inferpy/repo/InferPy/env2.7/lib/python2.7/site-packages/tensorflow/python/framework/common_shapes.py", line 691, in _call_cpp_shape_fn_impl
    raise ValueError(err.message)
ValueError: Cannot reshape a tensor with 2 elements to shape [1] (1 elements) for 'Reshape_25' (op: 'Reshape') with input shapes: [1,2], [1] and with input tensors computed as partial shapes: input[1] = [1].

Traceback (most recent call last):
File "", line 1, in
File "/Users/rcabanas/venv/2.7/inferpy2.7/lib/python2.7/site-packages/edward/models/random_variables.py", line 21, in init
_RandomVariable.init(self, *args, **kwargs)
File "/Users/rcabanas/venv/2.7/inferpy2.7/lib/python2.7/site-packages/edward/models/random_variable.py", line 133, in init
.format(self.class.name))
NotImplementedError: sample is not implemented for Binomial. You must either pass in the value argument or implement sample for Binomial.

Basic operators with RandomVariables

Remove modules from root module

parameter which are variables of dimension higher than 1

The code

t = inf.models.Normal(0.4,0.001,dim=2)
y = inf.models.Beta([t,0.5], 1)

raises the error:

ValueError: Shapes must be equal rank, but are 1 and 0
From merging shape 0 with other shapes. for 'stack_11' (op: 'Pack') with input shapes: [2], [].

Document pip installation

Type of posterior

Make consistent the distribution of the prior with the type of the posterior

simple dependent random variables

The parameters of a random variable might be others random variables.

Extend distribution collection

make documentation coherent with code

bug in probmodel constructor

The definition of a new probmodel keeps the variables of the previous models, i.e.:


with inf.ProbModel() as m:

    x = Normal(loc=1., scale=1., name="x", observed=True)
    y = Normal(loc=x, scale=1., dim=3, name="y")


with inf.ProbModel() as m2:

    x = Normal(loc=1., scale=1., name="x", observed=True)
    y = Normal(loc=x, scale=1., dim=3, name="y")


len(m2.varlist)   # 4 instead of 2

constant inf.INF_METHODS

change location of inf.INF_METHODS

ProbModel

Implement the class for encapsulating the functionality related to prob. models and for simplifying their definitions:

x = Normal(loc = 0, scale = 1, dim = 2)
y = Normal(loc = 2*x, scale = 1, dim = 2)

model = ProbModel(vars = [x, y])

https://docs.google.com/document/d/1W_JtNWHMnlZ3PwdnJYv3kFm6wee8qVMzuVuJ-r8Gd4c/edit?usp=sharing

Categorical dimension bug

t = inf.models.Normal(loc = [0.1, 0.4, 0.6], scale= 0.06)

x = inf.models.Categorical(probs = [t, 1])

x.dim

raises:

Traceback (most recent call last):
File "", line 1, in
File "/Users/rcabanas/Documents/UAL/inferpy/repo/InferPy/inferpy/models/random_variable.py", line 63, in dim
return self.base_object.shape.as_list()[-1]
IndexError: list index out of range

equal operator and boolean operators

Implement: ==, and, or

The result should be a deterministic variable

Simple inference algorithm

Implement a simple inference algorithm for learning the parameters from data:


model.compile(infMethod = 'KLqp')
model.fit(data_training, epochs=10)

x_post = model.posterior(x)

Normal distribution (basic)

The Normal distribution with location loc and scale parameters. Example:

x = Normal (loc = 0, scale = 1, dim = 5)

discussion details in https://docs.google.com/document/d/1eHoNEnyErCMoupzme5RJeqRIK2k63m4r4cjuxN95zTw/edit?usp=sharing

ALLOWED_VARS list

Implement an self-updated symbol in inferpy.models.ALLOWED_VARS

simplify seed management

In edward, the seed is set using:

ed.set_seed(1234)

though this command should be invoked at the begining. Make possible to update at any moment

infMethod argument

pca.compile(infMethod = 'KLqp', Q = qmodel)

Initial documentation

Write documentation in the code of the code implemented until now. Then synchronise it with sphinx

Reconsider sampling shapes

Related issue: #12

Plateau

Methods for defining variables with the plateau structure. For example:

with inf.replicate(size = K)
    x = Normal(loc = 0, scale = 1, dim = 2)

Initializer module

https://keras.io/initializers/

RandomVariable

Base class for random variables.

Factory tests

Create tests for multiple random variable types and shapes.

Deterministic variable

Implement Mixture

http://edwardlib.org/api/ed/models/Mixture

set seed in param_inference_test

Factory for random variables

Tests Normal and Replicate

Implement tests for Normal and Replicate classes. Make compatible with travis

shape parameter in distributions

matplotlib dependency

matplotlib 1.3.1 requires nose, which is not installed.
matplotlib 1.3.1 requires tornado, which is not installed.

QModel class

For example:

pca = ProbModel(vars = [mu,w_n,x_n])

q_mu = inf.inference.Q.Normal(bind = mu, initializer='random_unifrom')
q_w_n = inf.inference.Q.Normal(bind = w_n, initializer='random_unifrom')

qmodel = QModel(vars = [q_mu,q_w_n])

pca.compile(infMethod = 'KLqp', Q = qmodel)
pca.fit(x_train)
posterior_mu = pca.posterior(mu)

pip install inferpy

http://peterdowns.com/posts/first-time-with-pypi.html

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