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RandomForest.py
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RandomForest.py
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"""
@ Filename: RandomForest.py
@ Author: Ryuk
@ Create Date: 2019-07-09
@ Update Date: 2019-07-09
@ Description: Implement RandomForest
"""
import numpy as np
import operator as op
import pickle
from DecisionTree import DecisionTreeClassifier
from TreeRegression import RegressionTree
class RandomForestClassifier:
def __init__(self, tree_num=10, alpha=1e-5):
self.tree_num = tree_num
self.alpha=alpha
self.trees = []
self.prediction = None
self.probability = None
'''
Function: boostrap
Description: boostrap sampling and train a model
Input: train_data dataType: ndarray description: features
train_label dataType: ndarray description: labels
self dataType: obj description: the trained model
'''
def boostrap(self, train_data, train_label):
index = np.random.randint(0, len(train_data), (len(train_data)))
x = train_data[index]
y = train_label[index]
clf = DecisionTreeClassifier(t=self.alpha)
clf.train(x, y)
return clf
'''
Function: train
Description: train the model
Input: train_data dataType: ndarray description: features
train_label dataType: ndarray description: labels
Output: self dataType: obj description: the trained model
'''
def train(self, train_data, train_label):
for i in range(self.tree_num):
clf = self.boostrap(train_data, train_label)
self.trees.append(clf)
return self
'''
Function: vote
Description: return the label of the majority
Input: labels dataType: ndarray description: labels
Output: pred dataType: int description: prediction label of input vector
'''
def vote(self, labels):
label_count = {}
# get the counts of each label
for c in labels:
label_count[c] = label_count.get(c, 0) + 1
# get the labels of the majority
predition = sorted(label_count.items(), key=op.itemgetter(1), reverse=True)
pred = predition[0][0]
return pred
'''
Function: predict
Description: predict the testing set
Input: test_data dataType: ndarray description: features
Output: prediction dataType: ndarray description: the prediction results for testing set
'''
def predict(self, test_data):
labels = np.zeros([len(test_data), self.tree_num])
for i in range(self.tree_num):
clf = self.trees[i]
labels[:, i] = clf.predict(test_data).reshape(len(test_data))
prediction = np.zeros([len(test_data)])
for j in range(len(labels)):
prediction[j] = self.vote(labels[j,:])
self.prediction = prediction
return prediction
'''
Function: showDetectionResult
Description: show detection result
Input: test_data dataType: ndarray description: data for test
test_label dataType: ndarray description: labels of test data
Output: accuracy dataType: float description: detection accuarcy
'''
def accuarcy(self, test_label):
prediction = self.prediction
accuarcy = sum(prediction == test_label)/len(test_label)
return accuarcy
'''
Function: save
Description: save the model as pkl
Input: filename dataType: str description: the path to save model
'''
def save(self, filename):
f = open(filename, 'w')
model = self.trees
pickle.dump(model, f)
f.close()
'''
Function: load
Description: load the model
Input: filename dataType: str description: the path to save model
Output: self dataType: obj description: the trained model
'''
def load(self, filename):
f = open(filename)
self.trees = pickle.load(f)
return self
class RandomForestRegression:
def __init__(self, tree_num=10, error_threshold=1, N=4, alpha=0.01):
self.sample_num = 0
self.tree_num = tree_num
self.trees = []
self.error_threshold = error_threshold # the threshold of error
self.N = N # the least number of sample for split
self.alpha = alpha
self.tree_node = None
self.prediction = None
'''
Function: boostrap
Description: boostrap sampling and train a model
Input: train_data dataType: ndarray description: features
train_label dataType: ndarray description: labels
self dataType: obj description: the trained model
'''
def boostrap(self, train_data, train_label):
index = np.random.randint(0, self.sample_num, (self.sample_num))
x = train_data[index]
y = train_label[index]
clf = RegressionTree(error_threshold=1, N=4, alpha=0.01)
clf.train(x, y)
return clf
'''
Function: train
Description: train the model
Input: train_data dataType: ndarray description: features
train_label dataType: ndarray description: labels
Output: self dataType: obj description: the trained model
'''
def train(self, train_data, train_label):
for i in range(self.tree_num):
clf = self.boostrap(train_data, train_label)
self.trees.append(clf)
return self
'''
Function: vote
Description: return the label of the majority
Input: labels dataType: ndarray description: labels
Output: pred dataType: int description: prediction label of input vector
'''
def vote(self, labels):
label_count = {}
# get the counts of each label
for c in labels:
label_count[c] = label_count.get(c, 0) + 1
# get the labels of the majority
predition = sorted(label_count.items(), key=op.itemgetter(1), reverse=True)
pred = predition[0][0]
return pred
'''
Function: predict
Description: predict the testing set
Input: test_data dataType: ndarray description: features
Output: prediction dataType: ndarray description: the prediction results for testing set
'''
def predict(self, test_data):
labels = np.zeros([len(test_data), self.tree_num])
for i in range(self.tree_num):
labels[:,i] = self.trees[i].predict(test_data)
prediction = np.mean(labels, axis=0)
self.prediction = prediction
return prediction
'''
Function: showDetectionResult
Description: show detection result
Input: test_data dataType: ndarray description: data for test
test_label dataType: ndarray description: labels of test data
Output: accuracy dataType: float description: detection accuarcy
'''
def accuarcy(self, test_label):
test_label = np.expand_dims(test_label, axis=1)
prediction = self.prediction
accuarcy = sum(prediction == test_label)/len(test_label)
return accuarcy
'''
Function: save
Description: save the model as pkl
Input: filename dataType: str description: the path to save model
'''
def save(self, filename):
f = open(filename, 'w')
model = self.trees
pickle.dump(model, f)
f.close()
'''
Function: load
Description: load the model
Input: filename dataType: str description: the path to save model
Output: self dataType: obj description: the trained model
'''
def load(self, filename):
f = open(filename)
self.trees = pickle.load(f)
return self