def test_classifier_pickle(self): X = random(100) Y = X > 0.5 # pylint: disable=W0143 X = X.reshape((100, 1)) # pylint: disable=E1101 test_sklearn_pickle(lambda: LogisticRegression(), X, Y) test_sklearn_pickle(lambda: DecisionTreeLogisticRegression( fit_improve_algo=None), X, Y)
def test_piecewise_regressor_pickle(self): X = numpy.random.random(100) eps1 = (numpy.random.random(90) - 0.5) * 0.1 eps2 = numpy.random.random(10) * 2 eps = numpy.hstack([eps1, eps2]) X = X.reshape((100, 1)) # pylint: disable=E1101 Y = X.ravel() * 3.4 + 5.6 + eps test_sklearn_pickle(lambda: LinearRegression(), X, Y) test_sklearn_pickle(lambda: PiecewiseRegressor(), X, Y)
def test_quantile_regression_pickle(self): X = random(100) eps1 = (random(90) - 0.5) * 0.1 eps2 = random(10) * 2 eps = numpy.hstack([eps1, eps2]) X = X.reshape((100, 1)) # pylint: disable=E1101 Y = X.ravel() * 3.4 + 5.6 + eps test_sklearn_pickle(lambda: LinearRegression(), X, Y) test_sklearn_pickle(lambda: QuantileLinearRegression(), X, Y)
def test_classification_kmeans_pickle(self): iris = datasets.load_iris() X, y = iris.data, iris.target try: test_sklearn_pickle(lambda: ClassifierAfterKMeans(), X, y) except AttributeError as e: if compare_module_version(sklver, "0.24") < 0: return raise e
def test_quantile_regression_pickle(self): X = numpy.random.random(100) eps1 = (numpy.random.random(90) - 0.5) * 0.1 eps2 = numpy.random.random(10) * 2 eps = numpy.hstack([eps1, eps2]) X = X.reshape((100, 1)) # pylint: disable=E1101 Y = X.ravel() * 3.4 + 5.6 + eps test_sklearn_pickle(lambda: MLPRegressor( hidden_layer_sizes=(3,)), X, Y) test_sklearn_pickle(lambda: QuantileMLPRegressor( hidden_layer_sizes=(3,)), X, Y)
def test_transfer_transformer_pickle(self): X = numpy.array([[0.1], [0.2], [0.3], [0.4], [0.5]]) Y = numpy.array([1., 1.1, 1.2, 10, 1.4]) norm = StandardScaler() norm.fit(X) X2 = norm.transform(X) clr = LinearRegression() clr.fit(X2, Y) pipe = make_pipeline(TransferTransformer(norm), TransferTransformer(clr)) pipe.fit(X) test_sklearn_pickle(lambda: pipe, X, Y)
def test_predictable_tsne_pickle(self): iris = datasets.load_iris() X, y = iris.data[:20], iris.target[:20] test_sklearn_pickle(lambda: PredictableTSNE(), X, y)
def test_piecewise_classifier_pickle(self): X = random(100) Y = X > 0.5 # pylint: disable=W0143 X = X.reshape((100, 1)) # pylint: disable=E1101 test_sklearn_pickle(lambda: LogisticRegression(), X, Y) test_sklearn_pickle(lambda: PiecewiseClassifier(), X, Y)
def test_categories_to_integers_pickle(self): data = os.path.join(os.path.abspath(os.path.dirname(__file__)), "data", "adult_set.txt") df = pandas.read_csv(data, sep="\t") test_sklearn_pickle(lambda: CategoriesToIntegers(skip_errors=True), df)
def test_classification_kmeans_pickle(self): iris = datasets.load_iris() X, y = iris.data, iris.target test_sklearn_pickle(lambda: ClassifierAfterKMeans(), X, y)