CODEWITHSUNDEEP
pythonsvmscikit-learncross-validation
postgres
postgres

Reputation: 2260

TypeError: __init__() got an unexpected keyword argument 'scoring'

How is it possbile this demo code (taken from here: http://scikit-learn.org/dev/auto_examples/grid_search_digits.html)
TypeError: __init__() got an unexpected keyword argument 'scoring' when obviuodly scoring is a parameter (http://scikit-learn.org/dev/modules/generated/sklearn.grid_search.GridSearchCV.html#sklearn.grid_search.GridSearchCV)?

from __future__ import print_function

from sklearn import datasets
from sklearn.cross_validation import train_test_split
from sklearn.grid_search import GridSearchCV  
from sklearn.metrics import classification_report
from sklearn.svm import SVC

print(__doc__)

# Loading the Digits dataset
digits = datasets.load_digits()

# To apply an classifier on this data, we need to flatten the image, to
# turn the data in a (samples, feature) matrix:
n_samples = len(digits.images)
X = digits.images.reshape((n_samples, -1))
y = digits.target

# Split the dataset in two equal parts
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.5, random_state=0)

# Set the parameters by cross-validation
tuned_parameters = [{'kernel': ['rbf'], 'gamma': [1e-3, 1e-4],
                 'C': [1, 10, 100, 1000]},
                {'kernel': ['linear'], 'C': [1, 10, 100, 1000]}]

scores = ['precision', 'recall']

for score in scores:
    print("# Tuning hyper-parameters for %s" % score)
    print()

    clf = GridSearchCV(SVC(C=1), tuned_parameters, scoring=score)
    clf.fit(X_train, y_train, cv=5)

    print("Best parameters set found on development set:")
    print()
    print(clf.best_estimator_)
    print()
    print("Grid scores on development set:")
    print()
    for params, mean_score, scores in clf.grid_scores_:
        print("%0.3f (+/-%0.03f) for %r"
          % (mean_score, scores.std() / 2, params))
    print()

    print("Detailed classification report:")
    print()
    print("The model is trained on the full development set.")
    print("The scores are computed on the full evaluation set.")
    print()
    y_true, y_pred = y_test, clf.predict(X_test)
    print(classification_report(y_true, y_pred))
    print()

# Note the problem is too easy: the hyperparameter plateau is too flat and the
# output model is the same for precision and recall with ties in quality.

Upvotes: 3

Views: 7601

Answers (2)

John La Rooy
John La Rooy

Reputation: 304443

Are you running the development release?

For example, the parameter is not supported in 0.12

Upvotes: 1

Lie Ryan
Lie Ryan

Reputation: 64923

The parameter scoring is new in 0.14 development version and the example code is for that version. The scikit you have installed is probably version 0.13 or earlier, which does not have scoring parameter.

Upvotes: 7

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