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%matplotlib nbagg
import matplotlib.pyplot as plt
import numpy as np

Grid Searches

Grid-Search with build-in cross validation



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from sklearn.grid_search import GridSearchCV
from sklearn.svm import SVC



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from sklearn.datasets import load_digits
from sklearn.cross_validation import train_test_split
digits = load_digits()
X_train, X_test, y_train, y_test = train_test_split(digits.data,
                                                    digits.target)

Define parameter grid:



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import numpy as np

param_grid = {'C': 10. ** np.arange(-3, 3),
              'gamma' : 10. ** np.arange(-5, 0)}

np.set_printoptions(suppress=True)
print(param_grid)



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grid_search = GridSearchCV(SVC(), param_grid, verbose=3)

A GridSearchCV object behaves just like a normal classifier.



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grid_search.fit(X_train, y_train)



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grid_search.predict(X_test)



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grid_search.score(X_test, y_test)



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grid_search.best_params_



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# We extract just the scores

scores = [x.mean_validation_score for x in grid_search.grid_scores_]
scores = np.array(scores).reshape(6, 5)

plt.matshow(scores)
plt.xlabel('gamma')
plt.ylabel('C')
plt.colorbar()
plt.xticks(np.arange(5), param_grid['gamma'])
plt.yticks(np.arange(6), param_grid['C']);

Nested Cross-validation in scikit-learn:



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from sklearn.cross_validation import cross_val_score
cross_val_score(GridSearchCV(SVC(), param_grid),
                digits.data, digits.target)

Exercises

Use GridSearchCV to adjust n_neighbors of KNeighborsClassifier.



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# %load solutions/grid_search_k_neighbors.py