How to find out specific python error? - python-3.x

Question

I am writing the following classifier to check out sci-kit.
...
class MyClassifier():
def fit(self, x_train, y_train):
self.x_train = x_train
self.y_train = y_train
return
def predict(self, x_test):
prediction = []
for row in x_test:
label = self.closest(row)
prediction.append(label)
return prediction
def closest(self, row):
best_dist = euc(row, self.x_train[0])
best_index = 0
for i in range(1, len(self.x_train)):
dist = euc(row, self.x_train[0])
if dist < best_dist:
best_dist = dist
best_index = i
return self.y_train[best_index]
And later, I want to use my own classifier:
# Use my own Classifier
classifer = MyClassifier()
print(classifer)
classifer = classifer.fit(x_train, y_train)
prediction = classifer.predict(x_test)
print(prediction)
print(y_test)
When I run it, I am getting the following error:
<__main__.MyClassifier object at 0x103ec5668>
Traceback (most recent call last):
File "/.../NewClassifier.py", line 72, in <module>
prediction = classifer.predict(x_test)
AttributeError: 'NoneType' object has no attribute 'predict'
What's wrong with predict() function?

Answer 1

Your classmethod
def fit(self, x_train, y_train):
self.x_train = x_train
self.y_train = y_train
return
returns nothing, so it implicitly returns None.
Therefor classifer = classifer.fit(x_train, y_train) is overwrites the variable named classifer of type MyClassifier wiht a None.
A None has not method that you can call - thats the exact error message you got.
You should change classifer = classifer.fit(x_train, y_train) to simply
classifer.fit(x_train, y_train)
so you keep the variable named classifer as your Class-Instance instead of "overwriting" it with None.
This should fix it:
# Use my own Classifier
classifer = MyClassifier()
print(classifer)
classifer.fit(x_train, y_train)
prediction = classifer.predict(x_test)
print(prediction)
print(y_test)

Answer 2

I recommend using Python's built in debugger, pdb. If you add import pdb;pdb.set_trace() before your classifer = MyClassifier() statement, you can see every variable and interact with your code.
Now, you are overwriting your class instantiation.
-> print(classifer)
(Pdb) n
<__main__.MyClassifier object at 0x7f7fe2f139e8> // This is your classifer object
-> classifer = classifer.fit("test", "test2")
(Pdb) classifer
-> prediction = classifier.predict(x_test)
(Pdb) classifer
(Pdb)
So, because you are naming the variable the same thing, it's overwriting your previous class.
You have classifer = MyClassifier() and then classifer = classifer.foo so, it loses it's orginal reference to MyClassifier().
Secondly, your fit(x_train, y_train) function doesn't return anything.
Having:
def fit(self, x_train, y_train):
self.x_train = x_train
self.y_train = y_train
return
Is the same as:
def fit(self, x_train, y_train):
self.x_train = x_train
self.y_train = y_train
return None
Which is what your getting:
(Pdb) print(classifer)
None
And thus, that's why your receiving AttributeError: 'NoneType' object has no attribute 'predict' because classifer is None.
I'm not sure what the fit function is supposed to return, but I imagine it's self. So, the following code works for me in getting past your error, but since i don't know what x_train, y_train, x_test, and y_test are supposed to be, I couldn't run all of your code. Still, it fixes the problem you asked the question about.
class MyClassifier():
def fit(self, x_train, y_train):
self.x_train = x_train
self.y_train = y_train
return self // Must return something, and from context, this
// seems to be your intention.
def predict(self, x_test):
prediction = []
for row in x_test:
label = self.closest(row)
prediction.append(label)
return prediction
def closest(self, row):
best_dist = euc(row, self.x_train[0])
best_index = 0
for i in range(1, len(self.x_train)):
dist = euc(row, self.x_train)
if dist < best_dist:
best_dist = dist
best_index = i
return self.y_train[best_index]
classifier = MyClassifier()
print(classifier)
classifier2 = classifier.fit("test", "test2")
prediction = classifier2.predict(x_test)
print(prediction)
print(y_test)