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from sklearn import datasets
from sklearn.decomposition import PCA
from sklearn.pipeline import Pipeline
from sklearn.svm import SVC
grid = {
'svc__C': [0.001, 0.01, 0.05, 0.1, 0.3, 0.5, 0.7, 0.9, 1.0, 1.3, 1.5, 1.7, 10, 100, 1000],
'svc__kernel': ['linear', 'poly', 'rbf', 'sigmoid'],
'pca__n_components': [1, 2, 3, 4]
}
iris = datasets.load_iris()
X, y = iris.data, iris.target
pipeline = Pipeline(steps=[
('pca', PCA()),
('svc', SVC())
])
results = []
for n_iter in range(1, 20):
clf_ev = EvolutionaryAlgorithmSearchCV(pipeline, grid, scoring=None, verbose=True, n_jobs=4, population_size=5,
generations_number=n_iter)
clf_ev.fit(X, y)
results.append(clf_ev.best_score_)
print([x.values[0] for x in results])
from sklearn.grid_search import RandomizedSearchCV
results = []
for n_iter in range(1, 100):
clf_rand = RandomizedSearchCV(pipeline, grid, scoring=None, verbose=True, n_jobs=4,
n_iter=n_iter)
clf_rand.fit(X, y)
results.append(sorted(clf_rand.grid_scores_, key=lambda x: x[1])[-1])
print([x[1] for x in results])