CODEWITHSUNDEEP
pythonmachine-learningscikit-learn
Light Yagmi
Light Yagmi

Reputation: 5235

Cross validation with particular dataset lists with Python

I know sklearn has nice method to get cross validation scores:

 from sklearn.model_selection import cross_val_score
 clf = svm.SVC(kernel='linear', C=1)
 scores = cross_val_score(clf, iris.data, iris.target, cv=5)
 scores

I'd like to know scores with specific training and test set:

train_list = [train1, train2, train3] # train1,2,3 is the training data sets
test_list = [test1, test2, test3] # # test1,2,3 is the test data sets
clf = svm.SVC(kernel='linear', C=1)
scores = some_nice_method(clf, train_list, test_list)

Is there such kind of method giving scores of particular separated data set in python?

Upvotes: 3

Views: 1283

Answers (2)

Quazi Marufur Rahman
Quazi Marufur Rahman

Reputation: 2623

My suggestion is to use kfold cross validation like below. In this case, you will get both train, test indices for a particular split along with the accuracy score. In the new version of Sklearn, there are some changes. 

from sklearn import svm
from sklearn import datasets
from sklearn.model_selection import KFold
from sklearn.metrics import accuracy_score

iris = datasets.load_iris()
X = iris.data
y = iris.target

clf = svm.SVC(kernel='linear', C=1)
kf = KFold(n_splits=5)

for train_index, test_index in kf.split(range(len(X))):
    print("TRAIN:", train_index, "TEST:", test_index)
    X_train, X_test = X[train_index], X[test_index]
    y_train, y_test = y[train_index], y[test_index]
    clf.fit(X_train, y_train)
    y_pred = clf.predict(X_test)
    score = accuracy_score(y_test, y_pred)
    print score

Upvotes: 1

Ami Tavory
Ami Tavory

Reputation: 76336

This is exactly two lines of code:

for tr, te in zip(train_list, test_list):
    svm.SVC(kernel='linear', C=1).train(X[tr, :], y[tr]).score(X[te, :], y[te])

See sklearn.svn.SVC.score:

score(X, y, sample_weight=None)

Returns the mean accuracy on the given test data and labels.

Upvotes: 2

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