TypeError: Arrays must have consistent types in assignment

Question

Following up from here, I've got code like the following:

@jit(float_[:,:,:](float_[:,:], int_[:], int_)) 
def train_function(X, y, H):
    # do lots of stuff, including setting the arrays g and g_per_round like this:
    g = np.zeros((no_features, no_classes))
    g_per_round = np.zeros((H, no_features, no_classes))

    # do more stuff, then:    
        g_h = None
        j = 0
        print "Calculating regression coefficients per class. .."
        # building the parameters per j class
        for y1_w in zip(z.T, weights.T):
            y1, w = y1_w 
            temp_g = sm.WLS(y1, X, w).fit()  # Step 2(a)(ii)
            if g_h is None: # sometimes g *is* None, and that's fine
                   g_h = temp_g.params # this is an array of floats
            else:
                    g_h = np.c_[g_h, temp_g.params]
            j = j + 1

        if np.allclose(g,0) or g is None:
            g = g_h
        else:            
            g = g + g_h 

    # do lots more stuff, then finally:
    return g_per_round

class GentleBoostC(object):
    # init functions and stuff
    def train(self, X, y, H):
        self.g_per_round = train_function(X, y, H)

Now I'm getting the following error:

 @jit(float_[:,:,:](float_[:,:], int_[:], int_))
 more lines, etc etc etc, last few lines:
    unresolved_types, var_name)
  File "C:\Users\app\Anaconda\lib\site-packages\numba\typesystem\ssatypes.py", line 767, in promote_arrays
    assert_equal(non_array_types[0])
  File "C:\Users\app\Anaconda\lib\site-packages\numba\typesystem\ssatypes.py", line 764, in assert_equal
    var_name, result_type, other_type))
TypeError: Arrays must have consistent types in assignment for variable 'g': 'float64[:, :]' and 'none'

I actually had no issues with this before trying to add @jit to speed up my code.

Looks like g_h is None. It's possible that if your for loop never gets entered, then g_h will not get set to anything. — robbrit
– robbrit, Commented Sep 5, 2014 at 14:40
Why don't you output z or weights? If either of these is empty, then your for loop will never be entered. — robbrit
– robbrit, Commented Sep 5, 2014 at 14:46
I just edited the code to assign g some value when it's none. Also - I did try outputting weights and z, but the thing is - these aren't run time errors. I think these are compile errors from the numba package, even before the code starts running. The problem I think is in assigning a variable that's None to a variable that isn't None. Isn't there anyway around that in Numba? — user961627
– user961627, Commented Sep 5, 2014 at 14:49
Ahh I understand. Try initializing g_h to np.zeroes instead of None. — robbrit
– robbrit, Commented Sep 5, 2014 at 14:51

robbrit · Accepted Answer · 2014-09-05 15:02:04Z

2

The issue is that Numba is inferring g_h to be NoneType; initialize it to a vector and it will compile it properly:

g_h = np.zeroes((H, no_features, no_classes))

answered Sep 5, 2014 at 15:02

robbrit

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Francis Avila · Accepted Answer · 2014-09-05 15:05:05Z

2

The problem is that numba cannot know that g_h will not be None when it is finally assigned to g because the type of g_h depends on runtime flow control. In other words, if g_h could ever not be a float64, then it has to assume that sometimes isn't.

This is a documented limitation of numba and a limitation of type inference systems in general:

However, there are some restrictions, namely that variables must have a unifyable type at control flow merge points. For example, the following code will not compile:
@jit def incompatible_types(arg):
    if arg > 10:
        x = "hello"
    else:
        x = 1

    return x        # ERROR! Inconsistent type for x!

The solution is to initialize g_h to a compatible type instead of = None.

Numba's type inference is actually quite smart so you can mix types on a particular local variable without problems in many cases as long as the type can be unified before return. Read Numba documentation on types for more info.

answered Sep 5, 2014 at 15:05

Francis Avila

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1 Comment

user961627 Over a year ago

Thanks! If you have any experience with Numba, do you have any idea about this follow-up problem: stackoverflow.com/questions/25685916/…

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