Does the cross_val_predict
(see doc, v0.18) with k-fold method as shown in the code below calculate accuracy for each fold and average them finally or not?
cv = KFold(len(labels), n_folds=20)
clf = SVC()
ypred = cross_val_predict(clf, td, labels, cv=cv)
accuracy = accuracy_score(labels, ypred)
print accuracy
No, it does not!
According to cross validation doc page, cross_val_predict
does not return any scores but only the labels based on a certain strategy which is described here:
The function cross_val_predict has a similar interface to cross_val_score, but returns, for each element in the input, the prediction that was obtained for that element when it was in the test set. Only cross-validation strategies that assign all elements to a test set exactly once can be used (otherwise, an exception is raised).
And therefore by calling accuracy_score(labels, ypred)
you are just calculating accuracy scores of labels predicted by aforementioned particular strategy compared to the true labels. This again is specified in the same documentation page:
These prediction can then be used to evaluate the classifier:
predicted = cross_val_predict(clf, iris.data, iris.target, cv=10) metrics.accuracy_score(iris.target, predicted)
Note that the result of this computation may be slightly different from those obtained using cross_val_score as the elements are grouped in different ways.
If you need accuracy scores of different folds you should try:
>>> scores = cross_val_score(clf, X, y, cv=cv)
>>> scores
array([ 0.96..., 1. ..., 0.96..., 0.96..., 1. ])
and then for the mean accuracy of all folds use scores.mean()
:
>>> print("Accuracy: %0.2f (+/- %0.2f)" % (scores.mean(), scores.std() * 2))
Accuracy: 0.98 (+/- 0.03)
For calculating Cohen Kappa coefficient
and confusion matrix I assumed you mean kappa coefficient and confusion matrix between true labels and each fold's predicted labels:
from sklearn.model_selection import KFold
from sklearn.svm.classes import SVC
from sklearn.metrics.classification import cohen_kappa_score
from sklearn.metrics import confusion_matrix
cv = KFold(len(labels), n_folds=20)
clf = SVC()
for train_index, test_index in cv.split(X):
clf.fit(X[train_index], labels[train_index])
ypred = clf.predict(X[test_index])
kappa_score = cohen_kappa_score(labels[test_index], ypred)
confusion_matrix = confusion_matrix(labels[test_index], ypred)
cross_val_predict
return?It uses KFold to split the data to k
parts and then for i=1..k
iterations:
i'th
part as the test data and all other parts as training datai'th
)i'th
part (test data)In each iteration, label of i'th
part of data gets predicted. In the end cross_val_predict merges all partially predicted labels and returns them as the final result.
This code shows this process step by step:
X = np.array([[0], [1], [2], [3], [4], [5]])
labels = np.array(['a', 'a', 'a', 'b', 'b', 'b'])
cv = KFold(len(labels), n_folds=3)
clf = SVC()
ypred_all = np.chararray((labels.shape))
i = 1
for train_index, test_index in cv.split(X):
print("iteration", i, ":")
print("train indices:", train_index)
print("train data:", X[train_index])
print("test indices:", test_index)
print("test data:", X[test_index])
clf.fit(X[train_index], labels[train_index])
ypred = clf.predict(X[test_index])
print("predicted labels for data of indices", test_index, "are:", ypred)
ypred_all[test_index] = ypred
print("merged predicted labels:", ypred_all)
i = i+1
print("=====================================")
y_cross_val_predict = cross_val_predict(clf, X, labels, cv=cv)
print("predicted labels by cross_val_predict:", y_cross_val_predict)
The result is:
iteration 1 :
train indices: [2 3 4 5]
train data: [[2] [3] [4] [5]]
test indices: [0 1]
test data: [[0] [1]]
predicted labels for data of indices [0 1] are: ['b' 'b']
merged predicted labels: ['b' 'b' '' '' '' '']
=====================================
iteration 2 :
train indices: [0 1 4 5]
train data: [[0] [1] [4] [5]]
test indices: [2 3]
test data: [[2] [3]]
predicted labels for data of indices [2 3] are: ['a' 'b']
merged predicted labels: ['b' 'b' 'a' 'b' '' '']
=====================================
iteration 3 :
train indices: [0 1 2 3]
train data: [[0] [1] [2] [3]]
test indices: [4 5]
test data: [[4] [5]]
predicted labels for data of indices [4 5] are: ['a' 'a']
merged predicted labels: ['b' 'b' 'a' 'b' 'a' 'a']
=====================================
predicted labels by cross_val_predict: ['b' 'b' 'a' 'b' 'a' 'a']
As you can see from the code of cross_val_predict
on github, the function computes for each fold the predictions and concatenates them. The predictions are made based on model learned from other folds.
Here is a combination of your code and the example provided in the code
from sklearn import datasets, linear_model
from sklearn.model_selection import cross_val_predict, KFold
from sklearn.metrics import accuracy_score
diabetes = datasets.load_diabetes()
X = diabetes.data[:400]
y = diabetes.target[:400]
cv = KFold(n_splits=20)
lasso = linear_model.Lasso()
y_pred = cross_val_predict(lasso, X, y, cv=cv)
accuracy = accuracy_score(y_pred.astype(int), y.astype(int))
print(accuracy)
# >>> 0.0075
Finally, to answer your question: "No, the accuracy is not averaged for each fold"
As it is written in the documenattion sklearn.model_selection.cross_val_predict :
It is not appropriate to pass these predictions into an evaluation metric. Use cross_validate to measure generalization error.
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