def predict(self, X, quantile=None):
"""
Predict regression value for X.
Parameters
----------
X : array-like or sparse matrix of shape = [n_samples, n_features]
The input samples. Internally, it will be converted to
``dtype=np.float32`` and if a sparse matrix is provided
to a sparse ``csr_matrix``.
quantile : int, optional
Value ranging from 0 to 100. By default, the mean is returned.
check_input : boolean, (default=True)
Allow to bypass several input checking.
Don't use this parameter unless you know what you do.
Returns
-------
y : array of shape = [n_samples]
If quantile is set to None, then return E(Y | X). Else return
y such that F(Y=y | x) = quantile.
"""
# apply method requires X to be of dtype np.float32
X = check_array(X, dtype=np.float32, accept_sparse="csc")
if quantile is None:
return super(BaseForestQuantileRegressor, self).predict(X)
sorter = np.argsort(self.y_train_)
X_leaves = self.apply(X)
weights = np.zeros((X.shape[0], len(self.y_train_)))
quantiles = np.zeros((X.shape[0]))
for i, x_leaf in enumerate(X_leaves):
mask = self.y_train_leaves_ != np.expand_dims(x_leaf, 1)
x_weights = ma.masked_array(self.y_weights_, mask)
weights = x_weights.sum(axis=0)
quantiles[i] = weighted_percentile(
self.y_train_, quantile, weights, sorter)
return quantiles
评论列表
文章目录
