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)
