wehub-resource-sync
131 lines
3.7 KiB
Python
131 lines
3.7 KiB
Python
# coding:utf-8
|
|
import numpy as np
|
|
|
|
from mla.base import BaseEstimator
|
|
from mla.ensemble.base import information_gain, mse_criterion
|
|
from mla.ensemble.tree import Tree
|
|
|
|
|
|
class RandomForest(BaseEstimator):
|
|
def __init__(
|
|
self,
|
|
n_estimators=10,
|
|
max_features=None,
|
|
min_samples_split=10,
|
|
max_depth=None,
|
|
criterion=None,
|
|
):
|
|
"""Base class for RandomForest.
|
|
|
|
Parameters
|
|
----------
|
|
n_estimators : int
|
|
The number of decision tree.
|
|
max_features : int
|
|
The number of features to consider when looking for the best split.
|
|
min_samples_split : int
|
|
The minimum number of samples required to split an internal node.
|
|
max_depth : int
|
|
Maximum depth of the tree.
|
|
criterion : str
|
|
The function to measure the quality of a split.
|
|
"""
|
|
self.max_depth = max_depth
|
|
self.min_samples_split = min_samples_split
|
|
self.max_features = max_features
|
|
self.n_estimators = n_estimators
|
|
self.trees = []
|
|
|
|
def fit(self, X, y):
|
|
self._setup_input(X, y)
|
|
if self.max_features is None:
|
|
self.max_features = int(np.sqrt(X.shape[1]))
|
|
else:
|
|
assert X.shape[1] > self.max_features
|
|
self._train()
|
|
|
|
def _train(self):
|
|
for tree in self.trees:
|
|
tree.train(
|
|
self.X,
|
|
self.y,
|
|
max_features=self.max_features,
|
|
min_samples_split=self.min_samples_split,
|
|
max_depth=self.max_depth,
|
|
)
|
|
|
|
def _predict(self, X=None):
|
|
raise NotImplementedError()
|
|
|
|
|
|
class RandomForestClassifier(RandomForest):
|
|
def __init__(
|
|
self,
|
|
n_estimators=10,
|
|
max_features=None,
|
|
min_samples_split=10,
|
|
max_depth=None,
|
|
criterion="entropy",
|
|
):
|
|
super(RandomForestClassifier, self).__init__(
|
|
n_estimators=n_estimators,
|
|
max_features=max_features,
|
|
min_samples_split=min_samples_split,
|
|
max_depth=max_depth,
|
|
criterion=criterion,
|
|
)
|
|
|
|
if criterion == "entropy":
|
|
self.criterion = information_gain
|
|
else:
|
|
raise ValueError()
|
|
|
|
# Initialize empty trees
|
|
for _ in range(self.n_estimators):
|
|
self.trees.append(Tree(criterion=self.criterion))
|
|
|
|
def _predict(self, X=None):
|
|
y_shape = np.unique(self.y).shape[0]
|
|
predictions = np.zeros((X.shape[0], y_shape))
|
|
|
|
for i in range(X.shape[0]):
|
|
row_pred = np.zeros(y_shape)
|
|
for tree in self.trees:
|
|
row_pred += tree.predict_row(X[i, :])
|
|
|
|
row_pred /= self.n_estimators
|
|
predictions[i, :] = row_pred
|
|
return predictions
|
|
|
|
|
|
class RandomForestRegressor(RandomForest):
|
|
def __init__(
|
|
self,
|
|
n_estimators=10,
|
|
max_features=None,
|
|
min_samples_split=10,
|
|
max_depth=None,
|
|
criterion="mse",
|
|
):
|
|
super(RandomForestRegressor, self).__init__(
|
|
n_estimators=n_estimators,
|
|
max_features=max_features,
|
|
min_samples_split=min_samples_split,
|
|
max_depth=max_depth,
|
|
)
|
|
|
|
if criterion == "mse":
|
|
self.criterion = mse_criterion
|
|
else:
|
|
raise ValueError()
|
|
|
|
# Initialize empty regression trees
|
|
for _ in range(self.n_estimators):
|
|
self.trees.append(Tree(regression=True, criterion=self.criterion))
|
|
|
|
def _predict(self, X=None):
|
|
predictions = np.zeros((X.shape[0], self.n_estimators))
|
|
for i, tree in enumerate(self.trees):
|
|
predictions[:, i] = tree.predict(X)
|
|
return predictions.mean(axis=1)
|