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pyll's Issues
bug in replace_implicit_stochastic_nodes
In [8]: stochastic.sample(L.template, np.random)
UnboundLocalError Traceback (most recent call last)
/Users/dyamins/eccv12/ in ()
----> 1 stochastic.sample(L.template, np.random)
/Users/dyamins/eccv12/pyll/pyll/stochastic.pyc in sample(expr, rng)
199
200 def sample(expr, rng):
--> 201 foo, newrng = replace_implicit_stochastic_nodes(expr, as_apply(rng))
202 return rec_eval(foo)
203
/Users/dyamins/eccv12/pyll/pyll/stochastic.pyc in replace_implicit_stochastic_nodes(expr, rng, scope)
163 expr = draw
164 lrng = new_lrng
--> 165 return expr, new_lrng
166
167
UnboundLocalError: local variable 'new_lrng' referenced before assignment
reloading python modules
currently pyll does not play well with reload python modules ... you get lots of 'Cannot override existing symbol' errors
can this be improved?
namespaces!!!!!
This is very high priority if pyll is be used as a way to write lazy functional code.
how do I make references in pyll?
e.g. the old internal references from genson?
pos_args and named_args -> args
The AST nodes should be invariant to the calling convention (positional vs. named) used in the function calls that built the nodes.
args should be a dictionary mapping paramname -> argval
have a complete working replacement of genson and genson_helpers
For users of genson (such as myself), ... pyll is currently not a complete working replacement, so code that depends heavily on genson and hyperopt must stil use old versions of all that.
It is critical for moving to pyll (which I think would be a good idea sooner rather than later) that pyll support genson completely, with a simple drop-in replacement. Genson have a good interface for a variety of tasks -- especially sampling -- which pyll seems less suited to. These are important tasks for our purposes, I think.
I will assume that I will assign myself to this issue ...
support for non-string dict key
Crude way to reproduce:
In [36]: pyll.stochastic.sample({1:[s.uniform(0, 1), s.one_of(0, 1, 2)]}, np.random)
---------------------------------------------------------------------------
AssertionError Traceback (most recent call last)
/home/npinto/<ipython-input-36-6a0b951cbcec> in <module>()
----> 1 pyll.stochastic.sample({1:[s.uniform(0, 1), s.one_of(0, 1, 2)]}, np.random)
/home/npinto/venv/pyll-system/pyll/pyll/stochastic.py in sample(expr, rng)
154
155 def sample(expr, rng):
--> 156 foo = recursive_set_rng_kwarg(clone(as_apply(expr)), as_apply(rng))
157 return rec_eval(foo)
158
/home/npinto/venv/pyll-system/pyll/pyll/base.py in as_apply(obj)
132 items.sort()
133 named_args = [(k, as_apply(v)) for (k, v) in items]
--> 134 rval = Apply('dict', [], named_args, len(named_args))
135 else:
136 rval = Literal(obj)
/home/npinto/venv/pyll-system/pyll/pyll/base.py in __init__(self, name, pos_args, named_args, o_len)
154 assert all(isinstance(v, Apply) for v in pos_args)
155 assert all(isinstance(v, Apply) for k, v in named_args)
--> 156 assert all(isinstance(k, basestring) for k, v in named_args)
testcase for stochastic.sample
There is currently a line mentioning self
that cannot possibly be right. Test this function.
using replace_input / adding nodes
This is more of a "how to use pyll" question than an "issue":
Suppose I have a pyll object, e.g. a model spec generator. Suppose I want to programmatically replace one value in it with something determined by a function from some inputs. E.g. I want to set a key "num_filters" = pyll.scope.choice(function(some_inputs)), where the "num_filters" key is a node that appears somewhere down the object tree. I suspect I need to use replace_input? But the exact way to construct the arguments escapes me. Or do I need to do something else?
Similarly, suppose I want to add a key?
I guess I'm in general asking: how do I do simple transformations on pyll ASTs of this kind? If you could point me to the relevant place to figure it out, that would be great.
how do I sample a consistent random seem in pyll?
e.g. the replacement of
genson_template.sample(seed)
?
Right now,
stochastic.sample(template, np.random.RandomState(seed))
does NOT yield the same result on each call with a fixed seed, e.g. 0. Is it supposed to? If so, I'll submit a test
PyPi v0.0.1 release?
Hey James,
I'd like to package pyll (and later pyautodiff) for Gentoo and having a alpha version on PyPi would help. Would you be interested in pushing 0.0.1 releases of some of these packages on PyPi? It will also make requirements.txt
files simpler ;-)
Thanks!
n
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