I’m writing an AI state space search algorithm, and I have a generic class which can be used to quickly implement a search algorithm. A subclass would define the necessary operations, and the algorithm does the rest.

Here is where I get stuck: I want to avoid regenerating the parent state over and over again, so I have the following function, which returns the operations that can be legally applied to any state:

def get_operations(self, include_parent=True):
    ops = self._get_operations()
    if not include_parent and self.path.parent_op:
        try:
            parent_inverse = self.invert_op(self.path.parent_op)
            ops.remove(parent_inverse)
        except NotImplementedError:
            pass
    return ops

And the invert_op function throws by default.

Is there a faster way to check to see if the function is not defined than catching an exception?

I was thinking something on the lines of checking for present in dir, but that doesn’t seem right. hasattr is implemented by calling getattr and checking if it raises, which is not what I want.

Yes, use getattr() to get the attribute, and callable() to verify it is a method:

invert_op = getattr(self, "invert_op", None)
if callable(invert_op):
    invert_op(self.path.parent_op)

Note that getattr() normally throws exception when the attribute doesn’t exist. However, if you specify a default value (None, in this case), it will return that instead.

It works in both Python 2 and Python 3

hasattr(connection, 'invert_opt')

hasattr returns True if connection object has a function invert_opt defined. Here is the documentation for you to graze

https://docs.python.org/2/library/functions.html#hasattr https://docs.python.org/3/library/functions.html#hasattr

Is there a faster way to check to see if the function is not defined than catching an exception?

Why are you against that? In most Pythonic cases, it’s better to ask forgiveness than permission. ;-)

hasattr is implemented by calling getattr and checking if it raises, which is not what I want.

Again, why is that? The following is quite Pythonic:

    try:
        invert_op = self.invert_op
    except AttributeError:
        pass
    else:
        parent_inverse = invert_op(self.path.parent_op)
        ops.remove(parent_inverse)

Or,

    # if you supply the optional `default` parameter, no exception is thrown
    invert_op = getattr(self, 'invert_op', None)  
    if invert_op is not None:
        parent_inverse = invert_op(self.path.parent_op)
        ops.remove(parent_inverse)

Note, however, that getattr(obj, attr, default) is basically implemented by catching an exception, too. There is nothing wrong with that in Python land!

The responses herein check if a string is the name of an attribute of the object. An extra step (using callable) is needed to check if the attribute is a method.

So it boils down to: what is the fastest way to check if an object obj has an attribute attrib. The answer is

'attrib' in obj.__dict__

This is so because a dict hashes its keys so checking for the key’s existence is fast.

See timing comparisons below.

>>> class SomeClass():
...         pass
...
>>> obj = SomeClass()
>>>
>>> getattr(obj, "invert_op", None)
>>>
>>> %timeit getattr(obj, "invert_op", None)
1000000 loops, best of 3: 723 ns per loop
>>> %timeit hasattr(obj, "invert_op")
The slowest run took 4.60 times longer than the fastest. This could mean that an intermediate result is being cached.
1000000 loops, best of 3: 674 ns per loop
>>> %timeit "invert_op" in obj.__dict__
The slowest run took 12.19 times longer than the fastest. This could mean that an intermediate result is being cached.
10000000 loops, best of 3: 176 ns per loop

I like Nathan Ostgard’s answer and I up-voted it. But another way you could solve your problem would be to use a memoizing decorator, which would cache the result of the function call. So you can go ahead and have an expensive function that figures something out, but then when you call it over and over the subsequent calls are fast; the memoized version of the function looks up the arguments in a dict, finds the result in the dict from when the actual function computed the result, and returns the result right away.

Here is a recipe for a memoizing decorator called “lru_cache” by Raymond Hettinger. A version of this is now standard in the functools module in Python 3.2.

http://code.activestate.com/recipes/498245-lru-and-lfu-cache-decorators/

http://docs.python.org/release/3.2/library/functools.html

Like anything in Python, if you try hard enough, you can get at the guts and do something really nasty. Now, here’s the nasty part:

def invert_op(self, op):
    raise NotImplementedError

def is_invert_op_implemented(self):
    # Only works in CPython 2.x of course
    return self.invert_op.__code__.co_code == 't\x00\x00\x82\x01\x00d\x00\x00S'

Please do us a favor, just keep doing what you have in your question and DON’T ever use this unless you are on the PyPy team hacking into the Python interpreter. What you have up there is Pythonic, what I have here is pure EVIL.

You can also go over the class:

import inspect


def get_methods(cls_):
    methods = inspect.getmembers(cls_, inspect.isfunction)
    return dict(methods)

# Example
class A(object):
    pass

class B(object):
    def foo():
        print('B')


# If you only have an object, you can use `cls_ = obj.__class__`
if 'foo' in get_methods(A):
    print('A has foo')

if 'foo' in get_methods(B):
    print('B has foo')

While checking for attributes in __dict__ property is really fast, you cannot use this for methods, since they do not appear in __dict__ hash. You could however resort to hackish workaround in your class, if performance is that critical:

class Test():
    def __init__():
        # redefine your method as attribute
        self.custom_method = self.custom_method

    def custom_method(self):
        pass

Then check for method as:

t = Test()
'custom_method' in t.__dict__

Time comparision with getattr:

>>%timeit 'custom_method' in t.__dict__
55.9 ns ± 0.626 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each)

>>%timeit getattr(t, 'custom_method', None)
116 ns ± 0.765 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each)

Not that I’m encouraging this approach, but it seems to work.

[EDIT] Performance boost is even higher when method name is not in given class:

>>%timeit 'rubbish' in t.__dict__
65.5 ns ± 11 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each)

>>%timeit getattr(t, 'rubbish', None)
385 ns ± 12.9 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)

I’m trying to learn python and I now I am trying to get the hang of classes and how to manipulate them with instances.

I can’t seem to understand this practice problem:

Create and return a student object whose name, age, and major are the same as those given as input

def make_student(name, age, major)

I just don’t get what it means by object, do they mean I should create an array inside the function that holds these values? or create a class and let this function be inside it, and assign instances? (before this question i was asked to set up a student class with name, age, and major inside)

class Student:
    name = "Unknown name"
    age = 0
    major = "Unknown major"

class Student(object):
    name = ""
    age = 0
    major = ""

    # The class "constructor" - It's actually an initializer 
    def __init__(self, name, age, major):
        self.name = name
        self.age = age
        self.major = major

def make_student(name, age, major):
    student = Student(name, age, major)
    return student

Note that even though one of the principles in Python’s philosophy is “there should be one—and preferably only one—obvious way to do it”, there are still multiple ways to do this. You can also use the two following snippets of code to take advantage of Python’s dynamic capabilities:

class Student(object):
    name = ""
    age = 0
    major = ""

def make_student(name, age, major):
    student = Student()
    student.name = name
    student.age = age
    student.major = major
    # Note: I didn't need to create a variable in the class definition before doing this.
    student.gpa = float(4.0)
    return student

I prefer the former, but there are instances where the latter can be useful – one being when working with document databases like MongoDB.

Create a class and give it an __init__ method:

class Student:
    def __init__(self, name, age, major):
        self.name = name
        self.age = age
        self.major = major

    def is_old(self):
        return self.age > 100

Now, you can initialize an instance of the Student class:

>>> s = Student('John', 88, None)
>>> s.name
    'John'
>>> s.age
    88

Although I’m not sure why you need a make_student student function if it does the same thing as Student.__init__.

Objects are instances of classes. Classes are just the blueprints for objects. So given your class definition –

# Note the added (object) - this is the preferred way of creating new classes
class Student(object):
    name = "Unknown name"
    age = 0
    major = "Unknown major"

You can create a make_student function by explicitly assigning the attributes to a new instance of Student –

def make_student(name, age, major):
    student = Student()
    student.name = name
    student.age = age
    student.major = major
    return student

But it probably makes more sense to do this in a constructor (__init__) –

class Student(object):
    def __init__(self, name="Unknown name", age=0, major="Unknown major"):
        self.name = name
        self.age = age
        self.major = major

The constructor is called when you use Student(). It will take the arguments defined in the __init__ method. The constructor signature would now essentially be Student(name, age, major).

If you use that, then a make_student function is trivial (and superfluous) –

def make_student(name, age, major):
    return Student(name, age, major)

For fun, here is an example of how to create a make_student function without defining a class. Please do not try this at home.

def make_student(name, age, major):
    return type('Student', (object,),
                {'name': name, 'age': age, 'major': major})()

when you create an object using predefine class, at first you want to create a variable for storing that object. Then you can create object and store variable that you created.

class Student:
     def __init__(self):

# creating an object....

   student1=Student()

Actually this init method is the constructor of class.you can initialize that method using some attributes.. In that point , when you creating an object , you will have to pass some values for particular attributes..

class Student:
      def __init__(self,name,age):
            self.name=value
            self.age=value

 # creating an object.......

     student2=Student("smith",25)