Revision 24e7ea2b36c4516ab2c281d1460759f5420584c0 authored by James Hensman on 25 May 2016, 07:20:02 UTC, committed by James Hensman on 25 May 2016, 07:20:02 UTC
Syntax changes for Python 3 compatibility
model.py
from __future__ import print_function
from .param import Param, Parameterized
from scipy.optimize import minimize, OptimizeResult
import numpy as np
import tensorflow as tf
from . import hmc
import sys
class ObjectiveWrapper(object):
"""
A simple class to wrap the objective function in order to make it more robust.
The previosly seen state is cached so that we can easily acess it if the
model crashes.
"""
_previous_x = None
def __init__(self, objective):
self._objective = objective
def __call__(self, x):
f, g = self._objective(x)
g_is_fin = np.isfinite(g)
if np.all(g_is_fin):
self._previous_x = x # store the last known good value
return f, g
else:
print("Warning: inf or nan in gradient: replacing with zeros")
return f, np.where(g_is_fin, g, 0.)
from functools import wraps
class AutoFlow:
"""
This decorator-class is designed for use on methods of the Model class (below).
The idea is that methods that compute relevant quantities of the model
(such as predictions) can define a tf graph which we automatically run when
the (decorated) function is called.
The syntax looks like:
>>> class MyModel(Model):
>>>
>>> @AutoFlow(tf.placeholder(tf.float64), tf.placeholder(tf.float64))
>>> def my_method(self, x, y):
>>> #compute something, returning a tf graph.
>>> return tf.foo(self.baz, x, y)
>>> m = MyModel()
>>> x = np.random.randn(3,1)
>>> y = np.random.randn(3,1)
>>> result = my_method(x, y)
Now the output of the method call is the _result_ of the computation, equivalent to
>>> m = MyModel()
>>> x = np.random.randn(3,1)
>>> y = np.random.randn(3,1)
>>> x_tf = tf.placeholder(tf.float64)
>>> y_tf = tf.placeholder(tf.float64)
>>> with m.tf_mode():
>>> graph = tf.foo(m.baz, x_tf, y_tf)
>>> result = m._session.run(graph, feed_dict={x_tf:x, y_tf:y, m._free_vars:m.get_free_state()})
Not only is the syntax cleaner, but multiple calls to the method will
result in the graph being constructed only once.
"""
def __init__(self, *tf_args):
self.tf_args = tf_args
def __call__(self, tf_method):
@wraps(tf_method)
def runnable(instance, *np_args):
graph_name = '_' + tf_method.__name__ + '_graph'
if not hasattr(instance, graph_name):
with instance.tf_mode():
instance.make_tf_array(instance._free_vars)
graph = tf_method(instance, *self.tf_args)
setattr(instance, graph_name, graph)
feed_dict = dict(zip(self.tf_args, np_args))
feed_dict[instance._free_vars] = instance.get_free_state()
graph = getattr(instance, graph_name)
return instance._session.run(graph, feed_dict=feed_dict)
return runnable
class Model(Parameterized):
"""
The Model base class.
To use this class, inherriting classes must define the method
>>> build_likelihood(self)
which returns a tensorflow representation of the model likelihood.
Param and Parameterized objects that are children of the model can be used
in the tensorflow expression. Children on the model are defined by simply
doing:
>>> m = Model()
>>> p = Param(1.0)
>>> m.p = p
At compile time (i.e. when build_likelihood is called), the `Param` object
becomes a tensorflow variable.
The result of build_likelihood() is added to the prior (see Parameterized
class) and the resulting objective and gradients are compiled into
self._objective.
This object has a `_needs_recompile` switch. When any of the child nodes
change, this object is notified and on optimization (or mcmc) the
likelihood is recompiled. This allows fixing and constraining parameters,
but only recompiling lazily.
This object has a `_free_vars` tensorflow array. This array is ised to build
the tensorflow representations of the Param objects during `make_tf_array`.
This object defines `optimize` and `sample` to allow for model fitting.
"""
def __init__(self, name='model'):
"""
name is a string describing this model.
"""
Parameterized.__init__(self)
self._name = name
self._needs_recompile = True
self._free_vars = tf.placeholder('float64', name='free_vars')
self._session = tf.Session()
@property
def name(self):
return self._name
def _compile(self, optimizer=None):
"""
compile the tensorflow function "self._objective"
"""
# Make float32 hack
float32_hack = False
if optimizer is not None:
if tf.float64 not in optimizer._valid_dtypes() and tf.float32 in optimizer._valid_dtypes():
print("Using float32 hack for Tensorflow optimizers...")
float32_hack = True
self._free_vars = tf.Variable(self.get_free_state())
if float32_hack:
self._free_vars32 = tf.Variable(self.get_free_state().astype(np.float32))
self._free_vars = tf.cast(self._free_vars32, tf.float64)
self.make_tf_array(self._free_vars)
with self.tf_mode():
f = self.build_likelihood() + self.build_prior()
g, = tf.gradients(f, self._free_vars)
self._minusF = tf.neg( f, name = 'objective' )
self._minusG = tf.neg( g, name = 'grad_objective' )
# The optimiser needs to be part of the computational graph, and needs
# to be initialised before tf.initialise_all_variables() is called.
if optimizer is None:
opt_step = None
else:
if float32_hack:
opt_step = optimizer.minimize(tf.cast(self._minusF, tf.float32), var_list=[self._free_vars32])
else:
opt_step = optimizer.minimize(self._minusF, var_list=[self._free_vars])
init = tf.initialize_all_variables()
self._session.run(init)
#build tensorflow functions for computing the likelihood and predictions
print("compiling tensorflow function...")
sys.stdout.flush()
def obj(x):
return self._session.run([self._minusF, self._minusG], feed_dict={self._free_vars: x})
self._objective = obj
print("done")
sys.stdout.flush()
self._needs_recompile = False
return opt_step
def __setattr__(self, key, value):
Parameterized.__setattr__(self, key, value)
#delete any AutoFlow related graphs
if key=='_needs_recompile' and value:
for key in filter(lambda x : x[0]=='_' and x[-6:]=='_graph', dir(self)):
delattr(self, key)
@AutoFlow()
def compute_log_prior(self):
return self.build_prior()
@AutoFlow()
def compute_log_likelihood(self):
return self.build_likelihood()
def sample(self, num_samples, Lmax=20, epsilon=0.01, verbose=False):
"""
Use Hamiltonian Monte Carlo to draw samples from the posterior of the model.
"""
if self._needs_recompile:
self._compile()
return hmc.sample_HMC(self._objective, num_samples, Lmax, epsilon, x0=self.get_free_state(), verbose=verbose)
def optimize(self, method='L-BFGS-B', tol=None, callback=None, max_iters=1000, calc_feed_dict=None, **kw):
"""
Optimize the model by maximizing the likelihood (possibly with the
priors also) with respect to any free variables.
method can be one of:
a string, corresponding to a valid scipy.optimize.minimize optimizer
a tensorflow optimizer (e.g. tf.optimize.AdaGrad)
tol is the tolerance passed to scipy.optimize.minimize (ignored for tensorflow optimizers)
The callback function is executed by passing the current value of self.get_free_state()
max_iters defines the maximum number of iterations
calc_feed_dict is an optional function which returns a dictionary
(suitable for tf.Session.run's feed_dict argument)
In the case of the scipy optimization routines, any additional keyword
arguments are passed through.
KeyboardInterrupts are caught and the model is set to the most recent
value tried by the optimization routine.
This method returns the results of the call to optimize.minimize, or a
similar object in the tensorflow case.
"""
if type(method) is str:
return self._optimize_np(method, tol, callback, max_iters, **kw)
else:
return self._optimize_tf(method, callback, max_iters, calc_feed_dict, **kw)
def _optimize_tf(self, method, callback, max_iters, calc_feed_dict):
"""
Optimize the model using a tensorflow optimizer. see self.optimize()
"""
opt_step = self._compile(optimizer=method)
try:
iteration = 0
while iteration < max_iters:
if calc_feed_dict is None:
feed_dict = {}
else:
feed_dict = calc_feed_dict()
self._session.run(opt_step, feed_dict=feed_dict)
if callback is not None:
callback(self._session.run(self._free_vars))
iteration += 1
except KeyboardInterrupt:
print("Caught KeyboardInterrupt, setting model with most recent state.")
self.set_state(self._session.run(self._free_vars))
return None
final_x = self._session.run(self._free_vars)
self.set_state(final_x)
fun, jac = self._objective(final_x)
r = OptimizeResult(x=final_x,
success=True,
message="Finished iterations.",
fun=fun,
jac=jac,
status="Finished iterations.")
return r
def _optimize_np(self, method='L-BFGS-B', tol=None, callback=None, max_iters=1000, **kw):
"""
Optimize the model to find the maximum likelihood or MAP point. Here
we wrap `scipy.optimize.minimize`, any keyword arguments are passed
through as `options`.
method is a string (default 'L-BFGS-B') specifying the scipy optimization routine, one of
- 'Powell'
- 'CG'
- 'BFGS'
- 'Newton-CG'
- 'L-BFGS-B'
- 'TNC'
- 'COBYLA'
- 'SLSQP'
- 'dogleg'
tol is the tolerance to be pased to the optimization routine
callback is callback function to be passed to the optimization routine
max_iters is the maximum numebr of iterations (used in the options dict
for the optimization routine)
"""
if self._needs_recompile:
self._compile()
options=dict(display=True, max_iters=max_iters)
options.update(kw)
#LBFGS-B hcks. the options are different, annoyingly.
if method == 'L-BFGS-B':
options['maxiter'] = max_iters
del options['max_iters']
options['disp'] = options['display']
del options['display']
#here's the actual cll to minimize. cathc keyboard errors as harmless.
obj = ObjectiveWrapper(self._objective)
try:
result = minimize(fun=obj,
x0=self.get_free_state(),
method=method,
jac=True, # self._objective returns the objective and the jacobian.
tol=tol,
callback=callback,
options=options)
except (KeyboardInterrupt):
print("Caught KeyboardInterrupt, setting model with most recent state.")
self.set_state(obj._previous_x)
return None
print("optimization terminated, setting model state")
self.set_state(result.x)
return result
class GPModel(Model):
"""
A base class for Gaussian process models, that is, those of the form
theta ~ p(theta)
f ~ GP(m(x), k(x, x'; theta))
F = f(X)
Y|F ~ p(Y|F)
This class mostly adds functionality to compile predictions. To use it,
inherriting classes must define a build_predict function, which computes
the means and variances of the latent function. This gets compiled
similarly to build_likelihood in the Model class.
These predictions are then pushed through the likelihood to obtain means
and variances of held out data, self.predict_y.
The predictions can also be used to compute the (log) density of held-out
data via self.predict_density.
"""
def __init__(self, X, Y, kern, likelihood, mean_function, name='model'):
self.X, self.Y, self.kern, self.likelihood, self.mean_function = X, Y, kern, likelihood, mean_function
Model.__init__(self, name)
def build_predict(self):
raise NotImplementedError
@AutoFlow(tf.placeholder(tf.float64, [None, None]))
def predict_f(self, Xnew):
"""
Compute the mean and variance of the latent function(s) at the points Xnew
"""
return self.build_predict(Xnew)
@AutoFlow(tf.placeholder(tf.float64, [None, None]))
def predict_f_full_cov(self, Xnew):
"""
Compute the mean and covariance matrix of the latent function(s) at the
points Xnew.
"""
return self.build_predict(Xnew, full_cov=True)
@AutoFlow(tf.placeholder(tf.float64), tf.placeholder(tf.int32, []))
def predict_f_samples(self, Xnew, num_samples):
"""
Produce samples from the posterior latent function(s) at the points
Xnew.
"""
mu, var = self.build_predict(Xnew, full_cov=True)
samples = []
for i in range(self.num_latent):
L = tf.cholesky(var[:,:,i])
samples.append(mu[:,i:i+1] + tf.matmul(L, tf.random_normal(tf.pack([tf.shape(L)[0], num_samples]), dtype=tf.float64)))
return tf.transpose(tf.pack(samples))
@AutoFlow(tf.placeholder(tf.float64, [None, None]))
def predict_y(self, Xnew):
"""
Compute the mean and variance of held-out data at the points Xnew
"""
pred_f_mean, pred_f_var = self.build_predict(Xnew)
return self.likelihood.predict_mean_and_var(pred_f_mean, pred_f_var)
@AutoFlow(tf.placeholder(tf.float64, [None, None]), tf.placeholder(tf.float64, [None, None]))
def predict_density(self, Xnew, Ynew):
"""
Compute the (log) density of the data Ynew at the points Xnew
Note that this computes the log denisty of the data individually,
ignoring correlations between them. The result is a matrix the same
shape as Ynew containing the log densities.
"""
pred_f_mean, pred_f_var = self.build_predict(Xnew)
return self.likelihood.predict_density(pred_f_mean, pred_f_var, Ynew)
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