Python sklearn.gaussian_process.GaussianProcessClassifier() Examples

def test_custom_optimizer(kernel):
    # Test that GPC can use externally defined optimizers.
    # Define a dummy optimizer that simply tests 50 random hyperparameters
    def optimizer(obj_func, initial_theta, bounds):
        rng = np.random.RandomState(0)
        theta_opt, func_min = \
            initial_theta, obj_func(initial_theta, eval_gradient=False)
        for _ in range(50):
            theta = np.atleast_1d(rng.uniform(np.maximum(-2, bounds[:, 0]),
                                              np.minimum(1, bounds[:, 1])))
            f = obj_func(theta, eval_gradient=False)
            if f < func_min:
                theta_opt, func_min = theta, f
        return theta_opt, func_min

    gpc = GaussianProcessClassifier(kernel=kernel, optimizer=optimizer)
    gpc.fit(X, y_mc)
    # Checks that optimizer improved marginal likelihood
    assert_greater(gpc.log_marginal_likelihood(gpc.kernel_.theta),
                   gpc.log_marginal_likelihood(kernel.theta)) 

def test_custom_optimizer():
    # Test that GPC can use externally defined optimizers.
    # Define a dummy optimizer that simply tests 50 random hyperparameters
    def optimizer(obj_func, initial_theta, bounds):
        rng = np.random.RandomState(0)
        theta_opt, func_min = \
            initial_theta, obj_func(initial_theta, eval_gradient=False)
        for _ in range(50):
            theta = np.atleast_1d(rng.uniform(np.maximum(-2, bounds[:, 0]),
                                              np.minimum(1, bounds[:, 1])))
            f = obj_func(theta, eval_gradient=False)
            if f < func_min:
                theta_opt, func_min = theta, f
        return theta_opt, func_min

    for kernel in kernels:
        if kernel == fixed_kernel:
            continue
        gpc = GaussianProcessClassifier(kernel=kernel, optimizer=optimizer)
        gpc.fit(X, y_mc)
        # Checks that optimizer improved marginal likelihood
        assert_greater(gpc.log_marginal_likelihood(gpc.kernel_.theta),
                       gpc.log_marginal_likelihood(kernel.theta)) 

def test_objectmapper(self):
        df = pdml.ModelFrame([])
        dgp = df.gaussian_process

        self.assertIs(dgp.GaussianProcessClassifier,
                      gp.GaussianProcessClassifier)
        self.assertIs(dgp.GaussianProcessRegressor,
                      gp.GaussianProcessRegressor)
        self.assertIs(dgp.correlation_models.absolute_exponential,
                      gp.correlation_models.absolute_exponential)
        self.assertIs(dgp.correlation_models.squared_exponential,
                      gp.correlation_models.squared_exponential)
        self.assertIs(dgp.correlation_models.generalized_exponential,
                      gp.correlation_models.generalized_exponential)
        self.assertIs(dgp.correlation_models.pure_nugget,
                      gp.correlation_models.pure_nugget)
        self.assertIs(dgp.correlation_models.cubic,
                      gp.correlation_models.cubic)
        self.assertIs(dgp.correlation_models.linear,
                      gp.correlation_models.linear) 

def getModels():
    result = []
    result.append("LinearRegression")
    result.append("BayesianRidge")
    result.append("ARDRegression")
    result.append("ElasticNet")
    result.append("HuberRegressor")
    result.append("Lasso")
    result.append("LassoLars")
    result.append("Rigid")
    result.append("SGDRegressor")
    result.append("SVR")
    result.append("MLPClassifier")
    result.append("KNeighborsClassifier")
    result.append("SVC")
    result.append("GaussianProcessClassifier")
    result.append("DecisionTreeClassifier")
    result.append("RandomForestClassifier")
    result.append("AdaBoostClassifier")
    result.append("GaussianNB")
    result.append("LogisticRegression")
    result.append("QuadraticDiscriminantAnalysis")
    return result 

def test_multi_class_n_jobs():
    # Test that multi-class GPC produces identical results with n_jobs>1.
    for kernel in kernels:
        gpc = GaussianProcessClassifier(kernel=kernel)
        gpc.fit(X, y_mc)

        gpc_2 = GaussianProcessClassifier(kernel=kernel, n_jobs=2)
        gpc_2.fit(X, y_mc)

        y_prob = gpc.predict_proba(X2)
        y_prob_2 = gpc_2.predict_proba(X2)
        assert_almost_equal(y_prob, y_prob_2) 

def test_multi_class():
    # Test GPC for multi-class classification problems.
    for kernel in kernels:
        gpc = GaussianProcessClassifier(kernel=kernel)
        gpc.fit(X, y_mc)

        y_prob = gpc.predict_proba(X2)
        assert_almost_equal(y_prob.sum(1), 1)

        y_pred = gpc.predict(X2)
        assert_array_equal(np.argmax(y_prob, 1), y_pred) 

def test_lml_gradient():
    # Compare analytic and numeric gradient of log marginal likelihood.
    for kernel in kernels:
        gpc = GaussianProcessClassifier(kernel=kernel).fit(X, y)

        lml, lml_gradient = gpc.log_marginal_likelihood(kernel.theta, True)
        lml_gradient_approx = \
            approx_fprime(kernel.theta,
                          lambda theta: gpc.log_marginal_likelihood(theta,
                                                                    False),
                          1e-10)

        assert_almost_equal(lml_gradient, lml_gradient_approx, 3) 

def test_converged_to_local_maximum():
    # Test that we are in local maximum after hyperparameter-optimization.
    for kernel in kernels:
        if kernel == fixed_kernel:
            continue
        gpc = GaussianProcessClassifier(kernel=kernel).fit(X, y)

        lml, lml_gradient = \
            gpc.log_marginal_likelihood(gpc.kernel_.theta, True)

        assert_true(np.all((np.abs(lml_gradient) < 1e-4) |
                           (gpc.kernel_.theta == gpc.kernel_.bounds[:, 0]) |
                           (gpc.kernel_.theta == gpc.kernel_.bounds[:, 1]))) 

def test_lml_precomputed():
    # Test that lml of optimized kernel is stored correctly.
    for kernel in kernels:
        gpc = GaussianProcessClassifier(kernel=kernel).fit(X, y)
        assert_almost_equal(gpc.log_marginal_likelihood(gpc.kernel_.theta),
                            gpc.log_marginal_likelihood(), 7) 

def test_lml_improving():
    # Test that hyperparameter-tuning improves log-marginal likelihood.
    for kernel in kernels:
        if kernel == fixed_kernel:
            continue
        gpc = GaussianProcessClassifier(kernel=kernel).fit(X, y)
        assert_greater(gpc.log_marginal_likelihood(gpc.kernel_.theta),
                       gpc.log_marginal_likelihood(kernel.theta)) 

def test_predict_consistent():
    # Check binary predict decision has also predicted probability above 0.5.
    for kernel in kernels:
        gpc = GaussianProcessClassifier(kernel=kernel).fit(X, y)
        assert_array_equal(gpc.predict(X),
                           gpc.predict_proba(X)[:, 1] >= 0.5) 

def test_objectmapper_abbr(self):
        df = pdml.ModelFrame([])
        dgp = df.gp

        self.assertIs(dgp.GaussianProcessClassifier,
                      gp.GaussianProcessClassifier)
        self.assertIs(dgp.GaussianProcessRegressor,
                      gp.GaussianProcessRegressor) 

def __init__(self, **kwargs):
        super(GaussianProcess, self).__init__()
        super(GaussianProcess, self).SetModel(GaussianProcessClassifier(
            random_state=RANDOM_SEED[CLASSIFIER_GP], **kwargs)) 

def test_sklearn_50(self):
        iris = datasets.load_iris()
        irisd = pd.DataFrame(iris.data, columns=iris.feature_names)
        irisd['Species'] = iris.target
        target = 'Species'
        features = irisd.columns.drop('Species')
        f_name = "no_pipeline.pmml"
        model = GaussianProcessClassifier()
        model.fit(irisd[features], irisd[target])
        with self.assertRaises(TypeError):
            skl_to_pmml(model, features, target, f_name) 

def test_sklearn_49(self):
        iris = datasets.load_iris()
        irisd = pd.DataFrame(iris.data, columns=iris.feature_names)
        irisd['Species'] = iris.target
        target = 'Species'
        features = irisd.columns.drop('Species')
        f_name = "gpc.pmml"
        model = GaussianProcessClassifier()
        pipeline_obj = Pipeline([
            ('model', model)
        ])
        pipeline_obj.fit(irisd[features], irisd[target])
        with self.assertRaises(NotImplementedError):
            skl_to_pmml(pipeline_obj, numpy.array(features), target, f_name) 

def test_multi_class_n_jobs(kernel):
    # Test that multi-class GPC produces identical results with n_jobs>1.
    gpc = GaussianProcessClassifier(kernel=kernel)
    gpc.fit(X, y_mc)

    gpc_2 = GaussianProcessClassifier(kernel=kernel, n_jobs=2)
    gpc_2.fit(X, y_mc)

    y_prob = gpc.predict_proba(X2)
    y_prob_2 = gpc_2.predict_proba(X2)
    assert_almost_equal(y_prob, y_prob_2) 

def test_multi_class(kernel):
    # Test GPC for multi-class classification problems.
    gpc = GaussianProcessClassifier(kernel=kernel)
    gpc.fit(X, y_mc)

    y_prob = gpc.predict_proba(X2)
    assert_almost_equal(y_prob.sum(1), 1)

    y_pred = gpc.predict(X2)
    assert_array_equal(np.argmax(y_prob, 1), y_pred) 

def test_lml_gradient(kernel):
    # Compare analytic and numeric gradient of log marginal likelihood.
    gpc = GaussianProcessClassifier(kernel=kernel).fit(X, y)

    lml, lml_gradient = gpc.log_marginal_likelihood(kernel.theta, True)
    lml_gradient_approx = \
        approx_fprime(kernel.theta,
                      lambda theta: gpc.log_marginal_likelihood(theta,
                                                                False),
                      1e-10)

    assert_almost_equal(lml_gradient, lml_gradient_approx, 3) 

def test_converged_to_local_maximum(kernel):
    # Test that we are in local maximum after hyperparameter-optimization.
    gpc = GaussianProcessClassifier(kernel=kernel).fit(X, y)

    lml, lml_gradient = \
        gpc.log_marginal_likelihood(gpc.kernel_.theta, True)

    assert np.all((np.abs(lml_gradient) < 1e-4) |
                  (gpc.kernel_.theta == gpc.kernel_.bounds[:, 0]) |
                  (gpc.kernel_.theta == gpc.kernel_.bounds[:, 1])) 

def test_lml_precomputed(kernel):
    # Test that lml of optimized kernel is stored correctly.
    gpc = GaussianProcessClassifier(kernel=kernel).fit(X, y)
    assert_almost_equal(gpc.log_marginal_likelihood(gpc.kernel_.theta),
                        gpc.log_marginal_likelihood(), 7) 

def test_lml_improving(kernel):
    # Test that hyperparameter-tuning improves log-marginal likelihood.
    gpc = GaussianProcessClassifier(kernel=kernel).fit(X, y)
    assert_greater(gpc.log_marginal_likelihood(gpc.kernel_.theta),
                   gpc.log_marginal_likelihood(kernel.theta)) 

def test_predict_consistent(kernel):
    # Check binary predict decision has also predicted probability above 0.5.
    gpc = GaussianProcessClassifier(kernel=kernel).fit(X, y)
    assert_array_equal(gpc.predict(X),
                       gpc.predict_proba(X)[:, 1] >= 0.5) 

def getSKLearnModel(modelName):
    if modelName == 'LinearRegression':
        model = linear_model.LinearRegression()
    elif modelName == 'BayesianRidge':
        model = linear_model.BayesianRidge()
    elif modelName == 'ARDRegression':
        model = linear_model.ARDRegression()
    elif modelName == 'ElasticNet':
        model = linear_model.ElasticNet()
    elif modelName == 'HuberRegressor':
        model = linear_model.HuberRegressor()
    elif modelName == 'Lasso':
        model = linear_model.Lasso()
    elif modelName == 'LassoLars':
        model = linear_model.LassoLars()
    elif modelName == 'Rigid':
        model = linear_model.Ridge()
    elif modelName == 'SGDRegressor':
        model = linear_model.SGDRegressor()
    elif modelName == 'SVR':
        model = SVR()
    elif modelName=='MLPClassifier':
        model = MLPClassifier()
    elif modelName=='KNeighborsClassifier':
        model = KNeighborsClassifier()
    elif modelName=='SVC':
        model = SVC()
    elif modelName=='GaussianProcessClassifier':
        model = GaussianProcessClassifier()
    elif modelName=='DecisionTreeClassifier':
        model = DecisionTreeClassifier()
    elif modelName=='RandomForestClassifier':
        model = RandomForestClassifier()
    elif modelName=='AdaBoostClassifier':
        model = AdaBoostClassifier()
    elif modelName=='GaussianNB':
        model = GaussianNB()
    elif modelName=='LogisticRegression':
        model = linear_model.LogisticRegression()
    elif modelName=='QuadraticDiscriminantAnalysis':
        model = QuadraticDiscriminantAnalysis()

    return model 

def get_model(model_or_name, threads=-1, classify=False, seed=0):
    regression_models = {
        'xgboost': (XGBRegressor(max_depth=6, n_jobs=threads, random_state=seed), 'XGBRegressor'),
        'lightgbm': (LGBMRegressor(n_jobs=threads, random_state=seed, verbose=-1), 'LGBMRegressor'),
        'randomforest': (RandomForestRegressor(n_estimators=100, n_jobs=threads), 'RandomForestRegressor'),
        'adaboost': (AdaBoostRegressor(), 'AdaBoostRegressor'),
        'linear': (LinearRegression(), 'LinearRegression'),
        'elasticnet': (ElasticNetCV(positive=True), 'ElasticNetCV'),
        'lasso': (LassoCV(positive=True), 'LassoCV'),
        'ridge': (Ridge(), 'Ridge'),

        'xgb.1k': (XGBRegressor(max_depth=6, n_estimators=1000, n_jobs=threads, random_state=seed), 'XGBRegressor.1K'),
        'xgb.10k': (XGBRegressor(max_depth=6, n_estimators=10000, n_jobs=threads, random_state=seed), 'XGBRegressor.10K'),
        'lgbm.1k': (LGBMRegressor(n_estimators=1000, n_jobs=threads, random_state=seed, verbose=-1), 'LGBMRegressor.1K'),
        'lgbm.10k': (LGBMRegressor(n_estimators=10000, n_jobs=threads, random_state=seed, verbose=-1), 'LGBMRegressor.10K'),
        'rf.1k': (RandomForestRegressor(n_estimators=1000, n_jobs=threads), 'RandomForestRegressor.1K'),
        'rf.10k': (RandomForestRegressor(n_estimators=10000, n_jobs=threads), 'RandomForestRegressor.10K')
    }

    classification_models = {
        'xgboost': (XGBClassifier(max_depth=6, n_jobs=threads, random_state=seed), 'XGBClassifier'),
        'lightgbm': (LGBMClassifier(n_jobs=threads, random_state=seed, verbose=-1), 'LGBMClassifier'),
        'randomforest': (RandomForestClassifier(n_estimators=100, n_jobs=threads), 'RandomForestClassifier'),
        'adaboost': (AdaBoostClassifier(), 'AdaBoostClassifier'),
        'logistic': (LogisticRegression(), 'LogisticRegression'),
        'gaussian': (GaussianProcessClassifier(), 'GaussianProcessClassifier'),
        'knn': (KNeighborsClassifier(), 'KNeighborsClassifier'),
        'bayes': (GaussianNB(), 'GaussianNB'),
        'svm': (SVC(), 'SVC'),

        'xgb.1k': (XGBClassifier(max_depth=6, n_estimators=1000, n_jobs=threads, random_state=seed), 'XGBClassifier.1K'),
        'xgb.10k': (XGBClassifier(max_depth=6, n_estimators=10000, n_jobs=threads, random_state=seed), 'XGBClassifier.10K'),
        'lgbm.1k': (LGBMClassifier(n_estimators=1000, n_jobs=threads, random_state=seed, verbose=-1), 'LGBMClassifier.1K'),
        'lgbm.10k': (LGBMClassifier(n_estimators=1000, n_jobs=threads, random_state=seed, verbose=-1), 'LGBMClassifier.10K'),
        'rf.1k': (RandomForestClassifier(n_estimators=1000, n_jobs=threads), 'RandomForestClassifier.1K'),
        'rf.10k': (RandomForestClassifier(n_estimators=10000, n_jobs=threads), 'RandomForestClassifier.10K')
    }

    if isinstance(model_or_name, str):
        if classify:
            model_and_name = classification_models.get(model_or_name.lower())
        else:
            model_and_name = regression_models.get(model_or_name.lower())
        if not model_and_name:
            raise Exception("unrecognized model: '{}'".format(model_or_name))
        else:
            model, name = model_and_name
    else:
        model = model_or_name
        name = re.search("\w+", str(model)).group(0)

    return model, name