def test_pickle():
    """Check pickability"""

    # Check the regressor
    est = SymbolicRegressor(generations=2, random_state=0)
    est.fit(boston.data[:100, :], boston.target[:100])
    score = est.score(boston.data[500:, :], boston.target[500:])
    pickle_object = pickle.dumps(est)

    est2 = pickle.loads(pickle_object)
    assert_equal(type(est2), est.__class__)
    score2 = est2.score(boston.data[500:, :], boston.target[500:])
    assert_equal(score, score2)

    # Check the transformer
    est = SymbolicTransformer(generations=2, random_state=0)
    est.fit(boston.data[:100, :], boston.target[:100])
    X_new = est.transform(boston.data[500:, :])
    pickle_object = pickle.dumps(est)

    est2 = pickle.loads(pickle_object)
    assert_equal(type(est2), est.__class__)
    X_new2 = est2.transform(boston.data[500:, :])
    assert_array_almost_equal(X_new, X_new2)

    # Check the classifier
    est = SymbolicClassifier(generations=2, random_state=0)
    est.fit(cancer.data[:100, :], cancer.target[:100])
    score = est.score(cancer.data[500:, :], cancer.target[500:])
    pickle_object = pickle.dumps(est)

    est2 = pickle.loads(pickle_object)
    assert_equal(type(est2), est.__class__)
    score2 = est2.score(cancer.data[500:, :], cancer.target[500:])
    assert_equal(score, score2)
示例#2
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def test_pickle():
    """Check pickability"""

    # Check the regressor
    est = SymbolicRegressor(generations=2, random_state=0)
    est.fit(boston.data[:100, :], boston.target[:100])
    score = est.score(boston.data[500:, :], boston.target[500:])
    pickle_object = pickle.dumps(est)

    est2 = pickle.loads(pickle_object)
    assert_equal(type(est2), est.__class__)
    score2 = est2.score(boston.data[500:, :], boston.target[500:])
    assert_equal(score, score2)

    # Check the transformer
    est = SymbolicTransformer(generations=2, random_state=0)
    est.fit(boston.data[:100, :], boston.target[:100])
    X_new = est.transform(boston.data[500:, :])
    pickle_object = pickle.dumps(est)

    est2 = pickle.loads(pickle_object)
    assert_equal(type(est2), est.__class__)
    X_new2 = est2.transform(boston.data[500:, :])
    assert_array_almost_equal(X_new, X_new2)
示例#3
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    x = scaling(x)

    est = Ridge()
    est.fit(x[:300, :], y[:300])
    print(est.score(x[300:, :], y[300:]))

    function_set = [
        'add', 'sub', 'mul', 'div', 'sqrt', 'log', 'abs', 'neg', 'inv', 'max',
        'min'
    ]

    gp = SymbolicRegressor(generations=20,
                           population_size=2000,
                           hall_of_fame=100,
                           n_components=10,
                           function_set=function_set,
                           parsimony_coefficient=0.0005,
                           max_samples=0.9,
                           verbose=1,
                           random_state=0,
                           n_jobs=3)

    gp.fit(x[:300, :], y[:300])

    gp_features = gp.transform(x)
    new_boston = np.hstack((x, gp_features))

    est = Ridge()
    est.fit(new_boston[:300, :], y[:300])
    print(est.score(new_boston[300:, :], y[300:]))