class DecisionTreeSVRRegressor():
#class that hold svr model in the tree leaves
#implements all of DecisionTree methods and properties
def __init__(self,
params_svr = {'kernel': 'rbf', 'epsilon': 0.2},
criterion="mse",
splitter="best",
max_depth=None,
min_samples_split=2,
min_samples_leaf=1,
min_weight_fraction_leaf=0.,
max_features=None,
random_state=None,
max_leaf_nodes=None,
min_impurity_split=1e-7,
presort=False):
self.base_tree = DecisionTreeRegressor(
criterion=criterion,
splitter='best',
max_depth=max_depth,
min_samples_split=min_samples_split,
min_samples_leaf=min_samples_leaf,
min_weight_fraction_leaf=min_weight_fraction_leaf,
max_features=max_features,
max_leaf_nodes=max_leaf_nodes,
random_state=random_state,
presort=presort)
self.params_svr = params_svr
self.x_leaves = {}
self.y_leaves = {}
self.svrs_leaves = {}
self.tree_ = None
def fit(self, X, y, sample_weight=None, check_input=True,
X_idx_sorted=None):
self.base_tree.fit(X, y, sample_weight, check_input,
X_idx_sorted)
self.tree_ = self.base_tree.tree_
leaves = self.base_tree.apply(X)
for x_id in range(0, X.shape[0]):
leaf_id = int(leaves[x_id])
if not self.x_leaves.has_key(leaf_id):
self.x_leaves[leaf_id] = []
self.y_leaves[leaf_id] = []
self.x_leaves[leaf_id].append(X[x_id])
self. y_leaves[leaf_id].append(y[x_id])
for leaf_id in self.x_leaves.keys():
svr = SVR(**self.params_svr)
svr.fit(self.x_leaves[leaf_id], self.y_leaves[leaf_id])
self.svrs_leaves[leaf_id] = svr
return self
def predict(self, X, check_input=True):
y = []
x_leaves = self.base_tree.apply(X)
for x in X:
leaf = x_leaves[x]
svr = self.svrs_leaves[leaf]
pred = svr.predict(x)[0]
y.append(pred)
return y
def apply(self, X, check_input=True):
return self.base_tree.apply(X)
def decision_path(self, X, check_input=True):
return self.decision_path.apply(X)
def feature_importances_(self):
return self.decision_path.feature_importances_()
def _validate_X_predict(self, X, check_input=True):
return self.base_tree._validate_X_predict(X, check_input )
