def test_vcl_returning_for_boston_linear_regression(self):
boston = load_boston()
X, y = boston.data, boston.target
estimator = LinearRegression()
estimator.fit(X, y)
self.assertNotEqual(m2vcl.export_to_vcl(estimator), "")
def test_vcl_returning_for_nusvr(self):
n_samples, n_features = 10, 5
np.random.seed(0)
y = np.random.randn(n_samples)
X = np.random.randn(n_samples, n_features)
regr = NuSVR(C=1.0, nu=0.1)
regr.fit(X, y)
self.assertNotEqual(m2vcl.export_to_vcl(regr), "")
def test_vcl_returning_for_iris_decision_tree(self):
iris = load_iris()
X = iris.data
y = iris.target
X_train, X_test, y_train, y_test = train_test_split(
X, y, random_state=0)
clf = DecisionTreeClassifier(max_leaf_nodes=3, random_state=0)
clf.fit(X_train, y_train)
self.assertNotEqual(m2vcl.export_to_vcl(clf), "")
from sklearn.model_selection import train_test_split
from sklearn.datasets import load_iris
from sklearn.tree import DecisionTreeClassifier
import m2vcl
iris = load_iris()
X = iris.data
y = iris.target
X_train, X_test, y_train, y_test = train_test_split(
X, y, random_state=0)
clf = DecisionTreeClassifier(max_leaf_nodes=3, random_state=0)
clf.fit(X_train, y_train)
print(m2vcl.export_to_vcl(clf))
from sklearn.datasets import load_boston from sklearn.linear_model import LinearRegression import m2vcl boston = load_boston() X, y = boston.data, boston.target estimator = LinearRegression() estimator.fit(X, y) print(m2vcl.export_to_vcl(estimator))