If you’ve learned Python in a systematic way, you know that Python 2 has loose, if not poor, variable scope management. For example:

for i in range(5):
print i # still exists!

You shall not be surprised as many dynamic languages do so (I’ve checked Ruby and PHP). And in 99% of cases, there’s no problem with such behavior.

However, there’s still 1% left, which might drive you mad.

weird list comprehension

I’m taking an Artificial Intelligence course at present and a recent assignment is to implement various reinforcement learning algorithms, including Q-Learning. A strange problem arises that the Q-learning implementation works well when using generator expression, but fails miserably when using list comprehension.

The scenario is like this:

class QLearningAlgorithm(RLAlgorithm):
    """An implementation of Q-learning reinforcement learning algorithm."""

    def process_feedback(self, state, action, reward, new_state):
        if new_state is None:
            # current state is among end states
            V_new_state = 0.0 # the value of new_state
            possible_actions = self.get_actions(new_state)
            # this works
            V_new_state = max(self.computeQ(state, action) for action in possible_actions)
            # this doesn't work!
            # V_new_state = max([self.computeQ(state, action) for action in possible_actions])
        # code to update weights ...

I’ll omit the theoretical stuff and let’s focus on the lines with max function. If you know list comprehension and generator expression, using either one is equivalent to the other if fed to max. Having no clue, I checked the following possible factors:

  1. Does calling some dependency method like get_actions cause a state change? No.
  2. Does max handle list and generator differently? No.
  3. Do we have to put generator/list comprehension outside? The same result.
  4. (the guess is going wild…)

As someone with 4 years’ experience in Python (at least my resume says so), I was completely challenged. Luckily, I finally figured out what’s happening with dis.

the gotcha

I have to admit the mistake is a silly but hard to conceive one when you’re focusing on fighting the theoretical complexity. And clever you may have already detected the problem.

Knowing the issue, let’s consider a simpler case:

def all_squares_smaller_than_v1(numbers, i):
    """Check if all squares of numbers are smaller than i."""
    max_square = max([i**2 for i in numbers])
    return max_square < i

def all_squares_smaller_than_v2(numbers, i):
    """Check if all squares of numbers are smaller than i."""
    max_square = max(i**2 for i in numbers)
    return max_square < i

# prints False
print all_squares_smaller_than_v1([1, 2, 3], 10)
# prints True
print all_squares_smaller_than_v2([1, 2, 3], 10)

Like the problem above, the only difference is list comprehension vs. generator expression. And sadly, the list comprehension version fails again.

Why? Let’s dis it!

import dis
import your_module


The above is the basic usage of the dis module. And disassembling the byte code of the two function we get (v1 on the left and v2 on the right):

 41       0 LOAD_GLOBAL      0 (max)          | 46   0 LOAD_GLOBAL      0 (max)
          3 BUILD_LIST       0                |      3 LOAD_CONST       1 (<code object <genexpr> at 0x101275530, file "strange.py", line 46>)
          6 LOAD_FAST        0 (numbers)      |
          9 GET_ITER                          |
     >>  10 FOR_ITER        16 (to 29)        |      6 MAKE_FUNCTION    0
         13 STORE_FAST       1 (i)            |      9 LOAD_FAST        0 (numbers)
         16 LOAD_FAST        1 (i)            |     12 GET_ITER
         19 LOAD_CONST       1 (2)            |     13 CALL_FUNCTION    1
         22 BINARY_POWER                      |     16 CALL_FUNCTION    1
         23 LIST_APPEND      2                |     19 STORE_FAST       2 (max_square)
         26 JUMP_ABSOLUTE   10                |
     >>  29 CALL_FUNCTION    1                | 47  22 LOAD_FAST        2 (max_square)
         32 STORE_FAST       2 (max_square)   |     25 LOAD_FAST        1 (i)
                                              |     28 COMPARE_OP       0 (<)
 42      35 LOAD_FAST        2 (max_square)   |     31 RETURN_VALUE
         38 LOAD_FAST        1 (i)            |
         41 COMPARE_OP       0 (<)            |
         44 RETURN_VALUE

See? all_squares_smaller_than_v1 overrides variable i, which happens to be a function parameter (STORE_FAST writes value on top of stack to specified local variable). The poor Python variable scope comes back!

all_squares_smaller_than_v2 works fine as it does not involve any write to local variable. Actually, using generator expression creates a closure via MAKE_CLOSURE in more complex case. Let’s make a little change to x and see this in action:

def all_squares_smaller_than_v3(numbers, i):
    """Check if all squares of numbers are smaller than i."""
    square = lambda x: x**2
    max_square = max(square(i) for i in numbers)
    return max_square < i

In this version, a local variable is referenced in the generator function. If we disassemble this function, we get:

 51   0 LOAD_CONST       1 (<code object <lambda> at 0x10ff78db0, file "strange.py", line 51>)
      3 MAKE_FUNCTION    0
      6 STORE_DEREF      0 (square)

 52   9 LOAD_GLOBAL      0 (max)
     12 LOAD_CLOSURE     0 (square)
     15 BUILD_TUPLE      1
     18 LOAD_CONST       2 (<code object <genexpr> at 0x10ff78930, file "strange.py", line 52>)
     21 MAKE_CLOSURE     0
     24 LOAD_FAST        0 (numbers)
     27 GET_ITER
     28 CALL_FUNCTION    1
     31 CALL_FUNCTION    1
     34 STORE_FAST       2 (max_square)

 53  37 LOAD_FAST        2 (max_square)
     40 LOAD_FAST        1 (i)
     43 COMPARE_OP       0 (<)

It’s clear that only square is put in the closure because BUILD_TUPLE only takes 1 operand on top of the stack. It never modifies the parameter i.


I’ve also checked dict comprehension and set comprehension on Python 2.7.4 and it turns out they DO NOT alter local variable. So in Python 2 only list comprehension falls short! Plus, in Python 3 every type of comprehension is intact. I guess this is another incentive to move to Python 3.

So, lessons learned:

  1. Python 3 rocks!
  2. Prefer generator expression over list comprehension if they are equivalent.
  3. Avoid naming the temporary variable(s) the same as any parameter if you have to use list comprehension.
  4. dis always tells you the truth