def test_iris(self):
dataset = load_iris()
score = np.mean(
cross_val_score(DecisionTreeClassifier(tree_type=self.tree_type),
dataset.data,
dataset.target,
cv=10))
self.assertTrue(score > 0.8)
print('iris: tree_type: {}, score = {}'.format(self.tree_type, score))
import numpy as np
from sklearn.cross_validation import train_test_split, cross_val_score
from sklearn.datasets import load_boston, load_breast_cancer, load_iris, make_moons, make_gaussian_quantiles
from sklearn.metrics import mean_squared_error
from mlxtend.evaluate import plot_decision_regions
import matplotlib.pyplot as plt
from pines.estimators import DecisionTreeRegressor, DecisionTreeClassifier
from pines.tree_builders import TreeType
if __name__ == '__main__':
model = DecisionTreeClassifier(max_n_splits=3, max_depth=10, tree_type=TreeType.OBLIVIOUS)
X, y = make_gaussian_quantiles(n_samples=10000, n_classes=4)
model.fit(X, y)
print(model.tree_)
plot_decision_regions(X, y, clf=model, res=0.02, legend=2)
plt.savefig('decision_boundary.png')
import numpy as np
from sklearn.cross_validation import train_test_split, cross_val_score
from sklearn.datasets import load_boston, load_breast_cancer, load_iris, make_moons, make_gaussian_quantiles
from sklearn.metrics import mean_squared_error
from mlxtend.evaluate import plot_decision_regions
import matplotlib.pyplot as plt
from pines.estimators import DecisionTreeRegressor, DecisionTreeClassifier
from pines.tree_builders import TreeType
if __name__ == '__main__':
model = DecisionTreeClassifier(max_n_splits=3,
max_depth=10,
tree_type=TreeType.OBLIVIOUS)
X, y = make_gaussian_quantiles(n_samples=10000, n_classes=4)
model.fit(X, y)
print(model.tree_)
plot_decision_regions(X, y, clf=model, res=0.02, legend=2)
plt.savefig('decision_boundary.png')
algorithm to finish the construction.
The specific switch criterion is specified by `switch_criterion` option.
"""
import logging
import matplotlib.pyplot as plt
import numpy as np
from mlxtend.evaluate import plot_decision_regions
from sklearn.cross_validation import cross_val_score
from sklearn.datasets import make_moons, make_gaussian_quantiles
from pines.estimators import DecisionTreeClassifier
from pines.tree_builders import TreeType, ObliviousCartSwitchCriterionType, TreeBuilderObliviousCart
from pylab import rcParams
rcParams['figure.figsize'] = 20, 20
if __name__ == '__main__':
logging.basicConfig(level=logging.DEBUG)
TreeBuilderObliviousCart.debug = True
model = DecisionTreeClassifier(max_n_splits=10, max_depth=20, tree_type=TreeType.OBLIVIOUS_CART,
min_samples_per_leaf=30,
switch_criterion=ObliviousCartSwitchCriterionType.OBLIVIOUS_WHILE_CAN)
X, y = make_gaussian_quantiles(n_samples=2000, n_classes=4)
model.fit(X, y)
prediction = model.predict(X)
plot_decision_regions(X, y, clf=model, res=0.02, legend=2)
plt.savefig('decision_boundary.png')
