def growSkLearn(self, X, y):
"""
Grow a decision tree from sklearn.
"""
from sklearn.tree import DecisionTreeRegressor
regressor = DecisionTreeRegressor(max_depth=self.maxDepth,
min_samples_split=self.minSplit)
regressor.fit(X, y)
#Convert the sklearn tree into our tree
nodeId = (0, )
nodeStack = [(nodeId, 0)]
node = DecisionNode(numpy.arange(X.shape[0]), regressor.tree_.value[0])
self.tree.setVertex(nodeId, node)
while len(nodeStack) != 0:
nodeId, nodeInd = nodeStack.pop()
node = self.tree.getVertex(nodeId)
node.setError(regressor.tree_.best_error[nodeInd])
node.setFeatureInd(regressor.tree_.feature[nodeInd])
node.setThreshold(regressor.tree_.threshold[nodeInd])
if regressor.tree_.children[nodeInd, 0] != -1:
leftChildInds = node.getTrainInds()[
X[node.getTrainInds(),
node.getFeatureInd()] < node.getThreshold()]
leftChildId = self.getLeftChildId(nodeId)
leftChild = DecisionNode(
leftChildInds,
regressor.tree_.value[regressor.tree_.children[nodeInd,
0]])
self.tree.addChild(nodeId, leftChildId, leftChild)
nodeStack.append((self.getLeftChildId(nodeId),
regressor.tree_.children[nodeInd, 0]))
if regressor.tree_.children[nodeInd, 1] != -1:
rightChildInds = node.getTrainInds()[
X[node.getTrainInds(),
node.getFeatureInd()] >= node.getThreshold()]
rightChildId = self.getRightChildId(nodeId)
rightChild = DecisionNode(
rightChildInds,
regressor.tree_.value[regressor.tree_.children[nodeInd,
1]])
self.tree.addChild(nodeId, rightChildId, rightChild)
nodeStack.append((self.getRightChildId(nodeId),
regressor.tree_.children[nodeInd, 1]))
def growSkLearn(self, X, y):
"""
Grow a decision tree from sklearn.
"""
from sklearn.tree import DecisionTreeRegressor
regressor = DecisionTreeRegressor(max_depth = self.maxDepth, min_samples_split=self.minSplit)
regressor.fit(X, y)
#Convert the sklearn tree into our tree
nodeId = (0, )
nodeStack = [(nodeId, 0)]
node = DecisionNode(numpy.arange(X.shape[0]), regressor.tree_.value[0])
self.tree.setVertex(nodeId, node)
while len(nodeStack) != 0:
nodeId, nodeInd = nodeStack.pop()
node = self.tree.getVertex(nodeId)
node.setError(regressor.tree_.best_error[nodeInd])
node.setFeatureInd(regressor.tree_.feature[nodeInd])
node.setThreshold(regressor.tree_.threshold[nodeInd])
if regressor.tree_.children[nodeInd, 0] != -1:
leftChildInds = node.getTrainInds()[X[node.getTrainInds(), node.getFeatureInd()] < node.getThreshold()]
leftChildId = self.getLeftChildId(nodeId)
leftChild = DecisionNode(leftChildInds, regressor.tree_.value[regressor.tree_.children[nodeInd, 0]])
self.tree.addChild(nodeId, leftChildId, leftChild)
nodeStack.append((self.getLeftChildId(nodeId), regressor.tree_.children[nodeInd, 0]))
if regressor.tree_.children[nodeInd, 1] != -1:
rightChildInds = node.getTrainInds()[X[node.getTrainInds(), node.getFeatureInd()] >= node.getThreshold()]
rightChildId = self.getRightChildId(nodeId)
rightChild = DecisionNode(rightChildInds, regressor.tree_.value[regressor.tree_.children[nodeInd, 1]])
self.tree.addChild(nodeId, rightChildId, rightChild)
nodeStack.append((self.getRightChildId(nodeId), regressor.tree_.children[nodeInd, 1]))
