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RF.py
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RF.py
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# 该程序通过 tensorflow 的 tensor_forest API实现随机森林对随机数据的分类
# 参考程序：https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/2_BasicModels/random_forest.ipynb
# -*- coding: utf-8 -*-
"""
Created on 16 May, 2019
@author jswanglp
requirements:
numpy==1.15.4
tensorflow==1.12.0
scipy==1.1.0
hmmlearn==0.2.3
matplotlib==2.0.2
graphviz==0.14
scikit_learn==0.23.1
"""
import tensorflow as tf
from tensorflow.python.ops import resources
from tensorflow.contrib.tensor_forest.python import tensor_forest
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import numpy as np
import os
os.environ["CUDA_VISIBLE_DEVICES"] = ""
# 绑定数据
class Bunch(dict):
def __init__(self,*args,**kwds):
super(Bunch,self).__init__(*args,**kwds)
self.__dict__ = self
# 生成带标签的随机数据
def generate_random(sigma, N, mu1=[15., 25., 10], mu2=[30., 40., 30], mu3=[25., 10., 20], mu4=[40., 30., 40]):
c = sigma.shape[-1] #生成N行c维的随机测试数据
X = np.zeros((N, c)) # 初始化X，2行N列。2维数据，N个样本
target = np.zeros((N,1))
for i in range(N):
if np.random.random(1) < 0.25: # 生成0-1之间随机数
X[i, :] = np.random.multivariate_normal(mu1, sigma[0, :, :], 1) #用第一个高斯模型生成2维数据
target[i] = 0
elif 0.25 <= np.random.random(1) < 0.5:
X[i, :] = np.random.multivariate_normal(mu2, sigma[1, :, :], 1) #用第二个高斯模型生成2维数据
target[i] = 1
elif 0.5 <= np.random.random(1) < 0.75:
X[i, :] = np.random.multivariate_normal(mu3, sigma[2, :, :], 1) #用第三个高斯模型生成2维数据
target[i] = 2
else:
X[i, :] = np.random.multivariate_normal(mu4, sigma[3, :, :], 1) #用第四个高斯模型生成2维数据
target[i] = 3
return X, target
if __name__ == '__main__':
# 生成训练、测试集数据
k, N = 4, 400
sigma = np.zeros((k, 3, 3))
for i in range(k):
sigma[i, :, :] = np.diag(np.random.randint(10, 25, size=(3, )))
sample, target = generate_random(sigma, N)
feature_names = ['x_label', 'y_label', 'z_label'] # 特征数
target_names = ['gaussian1', 'gaussian2', 'gaussian3', 'gaussian4'] # 类别
data = Bunch(sample=sample, feature_names=feature_names, target=target, target_names=target_names)
sample_t, target_t = generate_random(sigma, N)
data_t = Bunch(sample=sample_t, target=target_t)
# 模型参数设置
num_steps = 20
batch_size = 256
num_classes = 4
num_features = 3
num_trees = 5
max_nodes = 20
X = tf.placeholder(tf.float32, shape=[None, num_features])
Y = tf.placeholder(tf.int32, shape=[None])
hparams = tensor_forest.ForestHParams(num_classes=num_classes,
num_features=num_features,
num_trees=num_trees,
max_nodes=max_nodes).fill()
# RF 的图模型
forest_graph = tensor_forest.RandomForestGraphs(hparams)
train_op = forest_graph.training_graph(X, Y)
loss_op = forest_graph.training_loss(X, Y)
# 正确率
infer_op, _, _ = forest_graph.inference_graph(X)
correct_prediction = tf.equal(tf.argmax(infer_op, 1), tf.cast(Y, tf.int64))
accuracy_op = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
# 初始化
init_vars = tf.group(tf.global_variables_initializer(),
resources.initialize_resources(resources.shared_resources()))
# 设置会话
sess = tf.train.MonitoredSession()
sess.run(init_vars)
# 训练模型
for i in range(1, num_steps + 1):
batch_data = np.c_[data.sample, data.target]
np.random.shuffle(batch_data)
batch_x, batch_y = batch_data[:batch_size, :-1], batch_data[:batch_size, -1]
_, l, acc = sess.run([train_op, loss_op, accuracy_op], feed_dict={X: batch_x, Y: batch_y})
acc *= 100
print_list = [i, l, acc]
print('Step {0[0]}, loss: {0[1]:.4f}, accuracy: {0[2]:.2f}%.'.format(print_list))
# 训练集
acc, pre_train = sess.run([accuracy_op, infer_op], feed_dict={X: data.sample, Y: data.target.flatten()})
acc *= 100
print('Accuracy on training set: %.2f.' % acc)
# 测试集
test_x, test_y = data_t.sample, data_t.target.flatten()
acc_t, pre_test = sess.run([accuracy_op, infer_op], feed_dict={X: test_x, Y: test_y})
acc_t *= 100
print('Accuracy on testing set: %.2f.' % acc_t)
sess.close()
# 显示训练，测试数据的分布
target_train = data.target.flatten().astype('int32')
target_pre = np.argmax(pre_train, axis=1).astype('int32')
titles = ['Random training data', 'Classified training data by RF']
TAR = [target_train, target_pre]
DATA = [data.sample, data.sample]
fig = plt.figure(1, figsize=(16, 8))
fig.subplots_adjust(wspace=.01, hspace=.02)
for i, title, data_n, tar in zip([1, 2], titles, DATA, TAR):
ax = fig.add_subplot(1, 2, i, projection='3d')
if title == 'Random training data':
ax.scatter(data_n[:,0], data_n[:,1], data_n[:,2], c='b', s=35, alpha=0.4, marker='o')
else:
color=['b','r','g','y']
for j in range(N):
ax.scatter(data_n[j, 0], data_n[j, 1], data_n[j, 2], c=color[tar[j]], s=35, alpha=0.4, marker='P')
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_zlabel('Z')
ax.view_init(elev=20., azim=-25)
ax.set_title(title, fontsize=14, y=0.01)
# plt.show()
# 显示 RF 对测试数据的分类情况
target_test = data_t.target.flatten().astype('int32')
target_pre = np.argmax(pre_test, axis=1).astype('int32')
titles = ['Random testing data', 'Classified testing data by RF']
TAR = [target_test, target_pre]
DATA = [data_t.sample, data_t.sample]
fig = plt.figure(2, figsize=(16, 8))
fig.subplots_adjust(wspace=.01, hspace=.02)
for i, title, data_n, tar in zip([1, 2], titles, DATA, TAR):
ax = fig.add_subplot(1, 2, i, projection='3d')
if title == 'Random testing data':
ax.scatter(data_n[:,0], data_n[:,1], data_n[:,2], c='b', s=35, alpha=0.4, marker='o')
else:
color=['b','r','g','y']
for j in range(N):
ax.scatter(data_n[j, 0], data_n[j, 1], data_n[j, 2], c=color[tar[j]], s=35, alpha=0.4, marker='P')
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_zlabel('Z')
ax.view_init(elev=20., azim=-25)
ax.set_title(title, fontsize=14, y=0.01)
plt.show()