Merge pull request #6 from goodfeli/add_informative_str

theano/printing.py

@@ -827,3 +827,128 @@ def pydotprint_variables(vars,
     g.write_png(outfile, prog='dot')
 
     print 'The output file is available at',outfile
+
+
+
+class _TagGenerator:
+    """ Class for giving abbreviated tags like to objects.
+        Only really intended for internal use in order to
+        implement min_informative_st """
+    def __init__(self):
+        self.cur_tag_number = 0
+
+    def get_tag(self):
+        rval = self.from_number(self.cur_tag_number)
+
+        self.cur_tag_number += 1
+
+        return rval
+
+    def from_number(self, number):
+        """ Converts number to string by rendering it in base 26 using
+            capital letters as digits """
+
+        base = 26
+
+        rval = ""
+
+        if number == 0:
+            rval = 'A'
+
+        while number != 0:
+            remainder = number % base
+            new_char = chr(ord('A')+remainder)
+            rval = new_char + rval
+            number /= base
+
+        return rval
+
+def min_informative_str(obj, indent_level = 0, _prev_obs = None, _tag_generator = None):
+    """
+    Returns a string specifying to the user what obj is
+    The string will print out as much of the graph as is needed
+    for the whole thing to be specified in terms only of constants
+    or named variables.
+
+
+    Parameters
+    ----------
+    obj: the name to convert to a string
+    indent_level: the number of tabs the tree should start printing at
+                  (nested levels of the tree will get more tabs)
+    _prev_obs: should only be used to by min_informative_str
+                    a dictionary mapping previously converted
+                    objects to short tags
+
+
+    Basic design philosophy
+    -----------------------
+    The idea behind this function is that it can be used as parts of command line tools
+    for debugging or for error messages. The information displayed is intended to be
+    concise and easily read by a human. In particular, it is intended to be informative
+    when working with large graphs composed of subgraphs from several different people's
+    code, as in pylearn2.
+
+    Stopping expanding subtrees when named variables are encountered makes it easier to
+    understand what is happening when a graph formed by composing several different graphs
+    made by code written by different authors has a bug.
+
+    An example output is:
+
+    A. Elemwise{add_no_inplace}
+        B. log_likelihood_v_given_h
+        C. log_likelihood_h
+
+
+    If the user is told they have a problem computing this value, it's obvious that either
+    log_likelihood_h or log_likelihood_v_given_h has the wrong dimensionality. The variable's
+    str object would only tell you that there was a problem with an Elemwise{add_no_inplace}.
+    Since there are many such ops in a typical graph, such an error message is considerably
+    less informative. Error messages based on this function should convey much more information
+    about the location in the graph of the error while remaining succint.
+
+    One final note: the use of capital letters to uniquely identify nodes within the graph
+    is motivated by legibility. I do not use numbers or lower case letters since these are
+    pretty common as parts of names of ops, etc. I also don't use the object's id like in
+    debugprint because it gives such a long string that takes time to visually diff.
+
+    """
+
+    if _prev_obs is None:
+        _prev_obs = {}
+
+    indent = '\t' * indent_level
+
+
+    if obj in _prev_obs:
+        tag = _prev_obs[obj]
+
+        return indent + '<' + tag + '>'
+
+    if _tag_generator is None:
+        _tag_generator = _TagGenerator()
+
+    cur_tag = _tag_generator.get_tag()
+
+    _prev_obs[obj] = cur_tag
+
+
+    if hasattr(obj, '__array__'):
+        name = '<ndarray>'
+    elif hasattr(obj, 'name') and obj.name is not None:
+        name = obj.name
+    elif hasattr(obj, 'owner') and obj.owner is not None:
+        name = str(obj.owner.op)
+        for ipt in obj.owner.inputs:
+            name += '\n' + min_informative_str(ipt,
+                    indent_level = indent_level + 1,
+                    _prev_obs = _prev_obs, _tag_generator = _tag_generator)
+    else:
+        name = str(obj)
+
+
+    prefix = cur_tag + '. '
+
+    rval = indent + prefix + name
+
+    return rval

theano/tests/test_printing.py

 """
-This is a REALLY PARTIAL TEST.
-
-I did them to help debug stuff.
-
+Tests of printing functionality
 """
 import logging
 import StringIO
@@ -10,8 +7,16 @@ import StringIO
 import theano
 import theano.tensor as tensor
 
+from theano.printing import min_informative_str
+
 
 def test_pydotprint_cond_highlight():
+    """
+    This is a REALLY PARTIAL TEST.
+
+    I did them to help debug stuff.
+    """
+
     x = tensor.dvector()
     f = theano.function([x], x*2)
     f([1,2,3,4])
@@ -30,3 +35,32 @@ def test_pydotprint_cond_highlight():
         theano.theano_logger.removeHandler(new_handler)
 
     assert s.getvalue() == 'pydotprint: cond_highlight is set but there is no IfElse node in the graph\n'
+
+
+def test_min_informative_str():
+    """ evaluates a reference output to make sure the
+        min_informative_str function works as intended """
+
+    A = tensor.matrix(name = 'A')
+    B = tensor.matrix(name = 'B')
+    C = A + B
+    C.name = 'C'
+    D = tensor.matrix(name = 'D')
+    E = tensor.matrix(name = 'E')
+
+    F = D + E
+    G = C + F
+
+    mis = min_informative_str(G)
+
+    reference = """A. Elemwise{add,no_inplace}
+	B. C
+	C. Elemwise{add,no_inplace}
+		D. D
+		E. E"""
+
+    if mis != reference:
+        print '--'+mis+'--'
+        print '--'+reference+'--'
+
+    assert mis == reference