Error in Implementation Cross Validation in KNN Python
I learned python KNN from scratch from this: https://machinelearningmastery.com/tutorial-to-implement-k-nearest-neighbors-in-python-from-scratch/
I want to implement Cross Validation from this:
https://machinelearningmastery.com/implement-backpropagation-algorithm-scratch-python/
Here is my implementation:
#Input
file = 'wdbc.csv'
fitur = 30
k = 2
n_folds = 2
#split = 0.80
#Proses
dataset = loadDataset(file, fitur, trainingSet, testSet)
folds = cross_validation_split(dataset, n_folds)
for fold in folds:
trainingSet = list(folds)
trainingSet.remove(fold)
trainingSet = sum(trainingSet, )
#print(trainingSet)
testSet = list()
predictions =
for row in fold:
row_copy = list(row)
testSet.append(row_copy)
#row_copy[-1] = None
for x in range(len(testSet)):
neighbors = getNeighbors(trainingSet, testSet[x], k)
result = getResponse(neighbors)
predictions.append(result)
But it's output has error:
File "C:/Users/user.compaq/Documents/help/cobaknn_crossval.py", line 45, in euclideanDistance
distance += math.pow((instance1[x] - instance2[x]), 2)
TypeError: unsupported operand type(s) for -: 'str' and 'float'
How can I fix this? All the instance1[x] and instance2[x] I've guaranteed them as float, but I don't know how the program identify one of them is string.
Help me. Thankyou.
python anaconda cross-validation knn
asked Nov 20 at 16:38
Yohanes Setiawan
337
add a comment |
I learned python KNN from scratch from this: https://machinelearningmastery.com/tutorial-to-implement-k-nearest-neighbors-in-python-from-scratch/
I want to implement Cross Validation from this:
https://machinelearningmastery.com/implement-backpropagation-algorithm-scratch-python/
Here is my implementation:
#Input
file = 'wdbc.csv'
fitur = 30
k = 2
n_folds = 2
#split = 0.80
#Proses
dataset = loadDataset(file, fitur, trainingSet, testSet)
folds = cross_validation_split(dataset, n_folds)
for fold in folds:
trainingSet = list(folds)
trainingSet.remove(fold)
trainingSet = sum(trainingSet, )
#print(trainingSet)
testSet = list()
predictions =
for row in fold:
row_copy = list(row)
testSet.append(row_copy)
#row_copy[-1] = None
for x in range(len(testSet)):
neighbors = getNeighbors(trainingSet, testSet[x], k)
result = getResponse(neighbors)
predictions.append(result)
But it's output has error:
File "C:/Users/user.compaq/Documents/help/cobaknn_crossval.py", line 45, in euclideanDistance
distance += math.pow((instance1[x] - instance2[x]), 2)
TypeError: unsupported operand type(s) for -: 'str' and 'float'
How can I fix this? All the instance1[x] and instance2[x] I've guaranteed them as float, but I don't know how the program identify one of them is string.
Help me. Thankyou.
python anaconda cross-validation knn
asked Nov 20 at 16:38
Yohanes Setiawan
337
I've seen this code before for loading the dataset. Are you sure you want to passfiturinstead ofsplitinto that function?
– LeKhan9
Nov 20 at 16:53
Also, what istrainingSet = sum(trainingSet, )supposed to accomplish? That will error out trying to concat a list to an int
– LeKhan9
Nov 20 at 16:57
the split has done with k-fold cross validation
– Yohanes Setiawan
Nov 20 at 17:13
add a comment |
I learned python KNN from scratch from this: https://machinelearningmastery.com/tutorial-to-implement-k-nearest-neighbors-in-python-from-scratch/
I want to implement Cross Validation from this:
https://machinelearningmastery.com/implement-backpropagation-algorithm-scratch-python/
Here is my implementation:
#Input
file = 'wdbc.csv'
fitur = 30
k = 2
n_folds = 2
#split = 0.80
#Proses
dataset = loadDataset(file, fitur, trainingSet, testSet)
folds = cross_validation_split(dataset, n_folds)
for fold in folds:
trainingSet = list(folds)
trainingSet.remove(fold)
trainingSet = sum(trainingSet, )
#print(trainingSet)
testSet = list()
predictions =
for row in fold:
row_copy = list(row)
testSet.append(row_copy)
#row_copy[-1] = None
for x in range(len(testSet)):
neighbors = getNeighbors(trainingSet, testSet[x], k)
result = getResponse(neighbors)
predictions.append(result)
But it's output has error:
File "C:/Users/user.compaq/Documents/help/cobaknn_crossval.py", line 45, in euclideanDistance
distance += math.pow((instance1[x] - instance2[x]), 2)
TypeError: unsupported operand type(s) for -: 'str' and 'float'
How can I fix this? All the instance1[x] and instance2[x] I've guaranteed them as float, but I don't know how the program identify one of them is string.
Help me. Thankyou.
python anaconda cross-validation knn
asked Nov 20 at 16:38
Yohanes Setiawan
337
I learned python KNN from scratch from this: https://machinelearningmastery.com/tutorial-to-implement-k-nearest-neighbors-in-python-from-scratch/
I want to implement Cross Validation from this:
https://machinelearningmastery.com/implement-backpropagation-algorithm-scratch-python/
Here is my implementation:
#Input
file = 'wdbc.csv'
fitur = 30
k = 2
n_folds = 2
#split = 0.80
#Proses
dataset = loadDataset(file, fitur, trainingSet, testSet)
folds = cross_validation_split(dataset, n_folds)
for fold in folds:
trainingSet = list(folds)
trainingSet.remove(fold)
trainingSet = sum(trainingSet, )
#print(trainingSet)
testSet = list()
predictions =
for row in fold:
row_copy = list(row)
testSet.append(row_copy)
#row_copy[-1] = None
for x in range(len(testSet)):
neighbors = getNeighbors(trainingSet, testSet[x], k)
result = getResponse(neighbors)
predictions.append(result)
But it's output has error:
File "C:/Users/user.compaq/Documents/help/cobaknn_crossval.py", line 45, in euclideanDistance
distance += math.pow((instance1[x] - instance2[x]), 2)
TypeError: unsupported operand type(s) for -: 'str' and 'float'
How can I fix this? All the instance1[x] and instance2[x] I've guaranteed them as float, but I don't know how the program identify one of them is string.
Help me. Thankyou.
python anaconda cross-validation knn
python anaconda cross-validation knn
asked Nov 20 at 16:38
Yohanes Setiawan
337
asked Nov 20 at 16:38
Yohanes Setiawan
337
asked Nov 20 at 16:38
Yohanes Setiawan
337
asked Nov 20 at 16:38
Yohanes Setiawan
337
asked Nov 20 at 16:38
Yohanes Setiawan
337
337
I've seen this code before for loading the dataset. Are you sure you want to passfiturinstead ofsplitinto that function?
– LeKhan9
Nov 20 at 16:53
Also, what istrainingSet = sum(trainingSet, )supposed to accomplish? That will error out trying to concat a list to an int
– LeKhan9
Nov 20 at 16:57
the split has done with k-fold cross validation
– Yohanes Setiawan
Nov 20 at 17:13
add a comment |
I've seen this code before for loading the dataset. Are you sure you want to passfiturinstead ofsplitinto that function?
– LeKhan9
Nov 20 at 16:53
Also, what istrainingSet = sum(trainingSet, )supposed to accomplish? That will error out trying to concat a list to an int
– LeKhan9
Nov 20 at 16:57
the split has done with k-fold cross validation
– Yohanes Setiawan
Nov 20 at 17:13
I've seen this code before for loading the dataset. Are you sure you want to pass
fitur instead of split into that function?– LeKhan9
Nov 20 at 16:53
I've seen this code before for loading the dataset. Are you sure you want to pass
fitur instead of split into that function?– LeKhan9
Nov 20 at 16:53
Also, what is
trainingSet = sum(trainingSet, ) supposed to accomplish? That will error out trying to concat a list to an int– LeKhan9
Nov 20 at 16:57
Also, what is
trainingSet = sum(trainingSet, ) supposed to accomplish? That will error out trying to concat a list to an int– LeKhan9
Nov 20 at 16:57
the split has done with k-fold cross validation
– Yohanes Setiawan
Nov 20 at 17:13
the split has done with k-fold cross validation
– Yohanes Setiawan
Nov 20 at 17:13
add a comment |
1 Answer
1
active
oldest
votes
Make sure you are feeding the model numerical values. Use something like:
trainingSet = np.array(trainingSet).astype(int)
And remove non-numerical values if you do find them.
answered Nov 20 at 16:43
yatu
4,1321423
add a comment |
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1 Answer
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1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
Make sure you are feeding the model numerical values. Use something like:
trainingSet = np.array(trainingSet).astype(int)
And remove non-numerical values if you do find them.
answered Nov 20 at 16:43
yatu
4,1321423
add a comment |
Make sure you are feeding the model numerical values. Use something like:
trainingSet = np.array(trainingSet).astype(int)
And remove non-numerical values if you do find them.
answered Nov 20 at 16:43
yatu
4,1321423
add a comment |
Make sure you are feeding the model numerical values. Use something like:
trainingSet = np.array(trainingSet).astype(int)
And remove non-numerical values if you do find them.
answered Nov 20 at 16:43
yatu
4,1321423
Make sure you are feeding the model numerical values. Use something like:
trainingSet = np.array(trainingSet).astype(int)
And remove non-numerical values if you do find them.
answered Nov 20 at 16:43
yatu
4,1321423
answered Nov 20 at 16:43
yatu
4,1321423
answered Nov 20 at 16:43
yatu
4,1321423
answered Nov 20 at 16:43
yatu
4,1321423
4,1321423
add a comment |
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I've seen this code before for loading the dataset. Are you sure you want to pass
fiturinstead ofsplitinto that function?– LeKhan9
Nov 20 at 16:53
Also, what is
trainingSet = sum(trainingSet, )supposed to accomplish? That will error out trying to concat a list to an int– LeKhan9
Nov 20 at 16:57
the split has done with k-fold cross validation
– Yohanes Setiawan
Nov 20 at 17:13