降维模型对比——PCA、ICA、TSNE

最新推荐文章于 2025-04-06 22:24:20 发布

行者无疆_ty

最新推荐文章于 2025-04-06 22:24:20 发布

阅读量4k

点赞数 1

分类专栏：人工智能文章标签：人工智能 tensorflow 神经网络深度学习 pca降维

本文链接：https://blog.youkuaiyun.com/a321123b/article/details/115791078

版权

人工智能专栏收录该内容

34 篇文章

订阅专栏

本文对比了PCA、ICA和TSNE三种降维方法在数据可视化和神经网络训练中的表现。通过实验，对不同模型的降维效果进行评估，展示了它们在处理数据集mock_kaggle.csv时的差异，并训练了基于降维数据的MLP神经网络，以验证降维质量。

摘要生成于 C知道，由 DeepSeek-R1 满血版支持，前往体验 >

本文使用PCA、ICA、TSNE等降维模型进行了对比实验，分别对原始数据进行降维并可视化展示，然后分别用PCA、ICA、TSNE降维后的数据训练一个简单MLP神经网络用于评估降维的数据质量。最后用三个模型的预测效果进行了对比和结果的展示。
本文使用的数据集在本人上传的资源中，链接为mock_kaggle.csv

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from sklearn.decomposition import FastICA,PCA
from sklearn.manifold import TSNE
from sklearn import preprocessing

取数据

data=pd.read_csv('mock_kaggle.csv',encoding ='gbk',parse_dates=['datetime'])

data=data.iloc[:,1:]

data

	特价	股票	价格
0	0	4972	1.29
1	70	4902	1.29
2	59	4843	1.29
3	93	4750	1.29
4	96	4654	1.29
...	...	...	...
932	98	3179	2.39
933	108	3071	2.39
934	128	4095	2.39
935	270	3825	2.39
936	183	3642	2.39

937 rows × 3 columns

标准化

data_normal = preprocessing.scale(data)
train_label=data_normal[:900,1]
test_label=data_normal[901:,1]

PCA降维

pca = PCA(n_components=2)
pca_data = pca.fit_transform(data_normal)     #等价于pca.fit(X) pca.transform(X)
#invX = pca.inverse_transform(X)  #将降维后的数据转换成原始数据
print(pca.explained_variance_ratio_)#输出降维后的各特征贡献度

[0.38909061 0.3429785 ]

plt.figure(figsize=(12,8))
plt.title('PCA Components')
plt.scatter(pca_data[:,0], pca_data[:,1])

在这里插入图片描述

训练简单神经网络

pca_train_data=pca_data[:900]
pca_test_data=pca_data[901:]
pca_model=tf.keras.Sequential([tf.keras.layers.Dense(10,input_shape=(2,),activation='relu'),
                           tf.keras.layers.Dense(10,activation='relu'),
                           tf.keras.layers.Dense(1)])
pca_model.summary()#查看模型基本信息
pca_model.compile(optimizer='adam',loss='mse',metrics=['mape']) #设置优化器和损失函数
pca_history=pca_model.fit(pca_train_data,train_label,epochs=100) #每个数据训练1000次

Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
dense (Dense)                (None, 10)                30        
_________________________________________________________________
dense_1 (Dense)              (None, 10)                110       
_________________________________________________________________
dense_2 (Dense)              (None, 1)                 11        
=================================================================
Total params: 151
Trainable params: 151
Non-trainable params: 0
_________________________________________________________________
Train on 900 samples
Epoch 1/100
900/900 [==============================] - 2s 2ms/sample - loss: 0.7850 - mape: 174.4778
Epoch 2/100
900/900 [==============================] - 0s 231us/sample - loss: 0.6291 - mape: 203.4696
Epoch 3/100
900/900 [==============================] - 0s 184us/sample - loss: 0.5221 - mape: 235.8750
Epoch 4/100
900/900 [==============================] - 0s 182us/sample - loss: 0.4389 - mape: 260.7603
Epoch 5/100
900/900 [==============================] - 0s 172us/sample - loss: 0.3774 - mape: 280.1786
Epoch 6/100
900/900 [==============================] - 0s 184us/sample - loss: 0.3348 - mape: 283.5222
Epoch 7/100
900/900 [==============================] - 0s 310us/sample - loss: 0.3014 - mape: 286.3506
Epoch 8/100
900/900 [==============================] - 0s 230us/sample - loss: 0.2762 - mape: 291.6097
Epoch 9/100
900/900 [==============================] - 0s 234us/sample - loss: 0.2539 - mape: 279.0452
Epoch 10/100
900/900 [==============================] - 0s 199us/sample - loss: 0.2364 - mape: 267.8730
Epoch 11/100
900/900 [==============================] - 0s 187us/sample - loss: 0.2219 - mape: 254.5653
Epoch 12/100
900/900 [==============================] - 0s 183us/sample - loss: 0.2095 - mape: 241.9442
Epoch 13/100
900/900 [==============================] - 0s 266us/sample - loss: 0.1987 - mape: 234.5619
Epoch 14/100
900/900 [==============================] - 0s 184us/sample - loss: 0.1884 - mape: 225.1507
Epoch 15/100
900/900 [==============================] - 0s 183us/sample - loss: 0.1811 - mape: 221.8909
Epoch 16/100
900/900 [==============================] - 0s 214us/sample - loss: 0.1749 - mape: 205.1877
Epoch 17/100
900/900 [==============================] - 0s 199us/sample - loss: 0.1702 - mape: 204.4365
Epoch 18/100
900/900 [==============================] - 0s 189us/sample - loss: 0.1649 - mape: 198.4753
Epoch 19/100
900/900 [==============================] - 0s 186us/sample - loss: 0.1621 - mape: 205.5184
Epoch 20/100
900/900 [==============================] - 0s 174us/sample - loss: 0.1590 - mape: 195.3820
Epoch 21/100
900/900 [==============================] - 0s 196us/sample - loss: 0.1569 - mape: 197.1003
Epoch 22/100
900/900 [==============================] - 0s 199us/sample - loss: 0.1547 - mape: 193.8486
Epoch 23/100
900/900 [==============================] - 0s 181us/sample - loss: 0.1543 - mape: 192.1595
Epoch 24/100
900/900 [==============================] - 0s 185us/sample - loss: 0.1519 - mape: 192.7433
Epoch 25/100
900/900 [==============================] - 0s 169us/sample - loss: 0.1519 - mape: 190.2121
Epoch 26/100
900/900 [==============================] - 0s 171us/sample - loss: 0.1508 - mape: 196.8424
Epoch 27/100
900/900 [==============================] - 0s 193us/sample - loss: 0.1510 - mape: 187.2915
Epoch 28/100
900/900 [==============================] - 0s 174us/sample - loss: 0.1489 - mape: 187.9957
Epoch 29/100
900/900 [==============================] - 0s 174us/sample - loss: 0.1494 - mape: 191.7432
Epoch 30/100
900/900 [==============================] - 0s 182us/sample - loss: 0.1485 - mape: 196.7090
Epoch 31/100
900/900 [==============================] - 0s 177us/sample - loss: 0.1477 - mape: 189.1148
Epoch 32/100
900/900 [==============================] - 0s 180us/sample - loss: 0.1468 - mape: 193.2476
Epoch 33/100
900/900 [==============================] - 0s 176us/sample - loss: 0.1473 - mape: 191.6133
Epoch 34/100
900/900 [==============================] - 0s 173us/sample - loss: 0.1464 - mape: 186.4275
Epoch 35/100
900/900 [==============================] - 0s 233us/sample - loss: 0.1454 - mape: 190.5075
Epoch 36/100
900/900 [==============================] - 0s 180us/sample - loss: 0.1450 - mape: 189.6160
Epoch 37/100
900/900 [==============================] - 0s 174us/sample - loss: 0.1450 - mape: 190.8441
Epoch 38/100
900/900 [==============================] - 0s 176us/sample - loss: 0.1449 - mape: 191.0887
Epoch 39/100
900/900 [==============================] - 0s 192us/sample - loss: 0.1446 - mape: 195.8095
Epoch 40/100
900/900 [==============================] - 0s 203us/sample - loss: 0.1446 - mape: 187.4518
Epoch 41/100
900/900 [==============================] - 0s 188us/sample - loss: 0.1441 - mape: 191.2747
Epoch 42/100
900/900 [==============================] - 0s 243us/sample - loss: 0.1441 - mape: 191.9978
Epoch 43/100
900/900 [==============================] - 0s 217us/sample - loss: 0.1439 - mape: 185.9973
Epoch 44/100
900/900 [==============================] - 0s 198us/sample - loss: 0.1446 - mape: 186.2656
Epoch 45/100
900/900 [==============================] - 0s 198us/sample - loss: 0.1439 - mape: 179.1912
Epoch 46/100
900/900 [==============================] - 0s 217us/sample - loss: 0.1443 - mape: 189.1231
Epoch 47/100
900/900 [==============================] - 0s 221us/sample - loss: 0.1446 - mape: 190.8205
Epoch 48/100
900/900 [==============================] - 0s 201us/sample - loss: 0.1426 - mape: 197.7570
Epoch 49/100
900/900 [==============================] - 0s 185us/sample - loss: 0.1425 - mape: 191.7054
Epoch 50/100
900/900 [==============================] - 0s 235us/sample - loss: 0.1424 - mape: 195.1246
Epoch 51/100
900/900 [==============================] - 0s 180us/sample - loss: 0.1417 - mape: 188.4568
Epoch 52/100
900/900 [==============================] - 0s 247us/sample - loss: 0.1422 - mape: 190.8196
Epoch 53/100
900/900 [==============================] - 0s 175us/sample - loss: 0.1419 - mape: 195.7927
Epoch 54/100
900/900 [==============================] - 0s 192us/sample - loss: 0.1421 - mape: 192.0233
Epoch 55/100
900/900 [==============================] - 0s 177us/sample - loss: 0.1416 - mape: 189.6909
Epoch 56/100
900/900 [==============================] - 0s 177us/sample - loss: 0.1410 - mape: 189.0969
Epoch 57/100
900/900 [==============================] - 0s 187us/sample - loss: 0.1415 - mape: 182.2992
Epoch 58/100
900/900 [==============================] - 0s 187us/sample - loss: 0.1412 - mape: 189.5773
Epoch 59/100
900/900 [==============================] - 0s 175us/sample - loss: 0.1417 - mape: 193.3024
Epoch 60/100
900/900 [==============================] - 0s 183us/sample - loss: 0.1411 - mape: 188.5672
Epoch 61/100
900/900 [==============================] - 0s 248us/sample - loss: 0.1422 - mape: 187.6868
Epoch 62/100
900/900 [==============================] - 0s 182us/sample - loss: 0.1424 - mape: 197.4033
Epoch 63/100
900/900 [==============================] - 0s 188us/sample - loss: 0.1425 - mape: 189.5185
Epoch 64/100
900/900 [==============================] - 0s 216us/sample - loss: 0.1411 - mape: 192.8488
Epoch 65/100
900/900 [==============================] - 0s 260us/sample - loss: 0.1413 - mape: 185.5174
Epoch 66/100
900/900 [==============================] - 0s 194us/sample - loss: 0.1405 - mape: 189.3971
Epoch 67/100
900/900 [==============================] - 0s 174us/sample - loss: 0.1407 - mape: 186.0119
Epoch 68/100
900/900 [==============================] - 0s 182us/sample - loss: 0.1430 - mape: 187.2527
Epoch 69/100
900/900 [==============================] - 0s 233us/sample - loss: 0.1410 - mape: 183.7318
Epoch 70/100
900/900 [==============================] - 0s 225us/sample - loss: 0.1409 - mape: 193.7119
Epoch 71/100
900/900 [==============================] - 0s 211us/sample - loss: 0.1402 - mape: 186.8175
Epoch 72/100
900/900 [==============================] - 0s 204us/sample - loss: 0.1400 - mape: 191.3500
Epoch 73/100
900/900 [==============================] - 0s 178us/sample - loss: 0.1396 - mape: 184.2501
Epoch 74/100
900/900 [==============================] - 0s 178us/sample - loss: 0.1411 - mape: 191.1308
Epoch 75/100
900/900 [==============================] - 0s 175us/sample - loss: 0.1399 - mape: 185.8354
Epoch 76/100
900/900 [==============================] - 0s 168us/sample - loss: 0.1397 - mape: 191.8246
Epoch 77/100
900/900 [==============================] - 0s 181us/sample - loss: 0.1395 - mape: 190.7547
Epoch 78/100
900/900 [==============================] - 0s 174us/sample - loss: 0.1397 - mape: 188.8087
Epoch 79/100
900/900 [==============================] - 0s 174us/sample - loss: 0.1399 - mape: 186.0689
Epoch 80/100
900/900 [==============================] - 0s 171us/sample - loss: 0.1397 - mape: 189.4801
Epoch 81/100
900/900 [==============================] - 0s 163us/sample - loss: 0.1396 - mape: 181.5564
Epoch 82/100
900/900 [==============================] - 0s 174us/sample - loss: 0.1398 - mape: 187.0418
Epoch 83/100
900/900 [==============================] - 0s 178us/sample - loss: 0.1393 - mape: 188.8249
Epoch 84/100
900/900 [==============================] - 0s 171us/sample - loss: 0.1419 - mape: 186.9317
Epoch 85/100
900/900 [==============================] - 0s 176us/sample - loss: 0.1400 - mape: 194.4677
Epoch 86/100
900/900 [==============================] - 0s 167us/sample - loss: 0.1401 - mape: 181.7322
Epoch 87/100
900/900 [==============================] - 0s 180us/sample - loss: 0.1401 - mape: 190.9029
Epoch 88/100
900/900 [==============================] - 0s 172us/sample - loss: 0.1405 - mape: 192.8174
Epoch 89/100
900/900 [==============================] - 0s 172us/sample - loss: 0.1400 - mape: 186.8127
Epoch 90/100
900/900 [==============================] - 0s 176us/sample - loss: 0.1398 - mape: 188.7201
Epoch 91/100
900/900 [==============================] - 0s 171us/sample - loss: 0.1407 - mape: 185.1754
Epoch 92/100
900/900 [==============================] - 0s 168us/sample - loss: 0.1397 - mape: 192.7381
Epoch 93/100
900/900 [==============================] - 0s 221us/sample - loss: 0.1393 - mape: 186.2105
Epoch 94/100
900/900 [==============================] - 0s 199us/sample - loss: 0.1391 - mape: 181.0793
Epoch 95/100
900/900 [==============================] - 0s 180us/sample - loss: 0.1389 - mape: 190.5739
Epoch 96/100
900/900 [==============================] - 0s 182us/sample - loss: 0.1382 - mape: 192.4536
Epoch 97/100
900/900 [==============================] - 0s 177us/sample - loss: 0.1386 - mape: 182.1012
Epoch 98/100
900/900 [==============================] - 0s 186us/sample - loss: 0.1386 - mape: 190.5573
Epoch 99/100
900/900 [==============================] - 0s 196us/sample - loss: 0.1379 - mape: 186.4868
Epoch 100/100
900/900 [==============================] - 0s 271us/sample - loss: 0.1384 - mape: 189.7631

ICA降维

# ICA降维
ica = FastICA(n_components=2)
ica_data = ica.fit_transform(data_normal)  # 重构信号
# A_ = ica.mixing_  # 获得估计混合后的矩阵

plt.figure(figsize=(12,8))
plt.title('ICA Components')
plt.scatter(ica_data[:,0], ica_data[:,1])

在这里插入图片描述

训练简单神经网络

ica_train_data=ica_data[:900]
ica_test_data=ica_data[901:]
ica_model=tf.keras.Sequential([tf.keras.layers.Dense(10,input_shape=(2,),activation='relu'),
                           tf.keras.layers.Dense(10,activation='relu'),
                           tf.keras.layers.Dense(1)])
ica_model.summary()#查看模型基本信息
ica_model.compile(optimizer='adam',loss='mse',metrics=['mape']) #设置优化器和损失函数
ica_history=ica_model.fit(ica_train_data,train_label,epochs=100) #每个数据训练1000次

Model: "sequential_1"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
dense_3 (Dense)              (None, 10)                30        
_________________________________________________________________
dense_4 (Dense)              (None, 10)                110       
_________________________________________________________________
dense_5 (Dense)              (None, 1)                 11        
=================================================================
Total params: 151
Trainable params: 151
Non-trainable params: 0
_________________________________________________________________
Train on 900 samples
Epoch 1/100
900/900 [==============================] - 1s 1ms/sample - loss: 0.9873 - mape: 103.0597
Epoch 2/100
900/900 [==============================] - 0s 183us/sample - loss: 0.9456 - mape: 110.8891
Epoch 3/100
900/900 [==============================] - 0s 170us/sample - loss: 0.9062 - mape: 117.8290
Epoch 4/100
900/900 [==============================] - 0s 177us/sample - loss: 0.8612 - mape: 120.4753
Epoch 5/100
900/900 [==============================] - 0s 183us/sample - loss: 0.8110 - mape: 122.2516
Epoch 6/100
900/900 [==============================] - 0s 185us/sample - loss: 0.7573 - mape: 134.9871
Epoch 7/100
900/900 [==============================] - 0s 176us/sample - loss: 0.6952 - mape: 135.3464
Epoch 8/100
900/900 [==============================] - 0s 174us/sample - loss: 0.6329 - mape: 152.0235
Epoch 9/100
900/900 [==============================] - 0s 186us/sample - loss: 0.5675 - mape: 162.2172
Epoch 10/100
900/900 [==============================] - 0s 198us/sample - loss: 0.5150 - mape: 168.6576
Epoch 11/100
900/900 [==============================] - 0s 205us/sample - loss: 0.4536 - mape: 201.6551
Epoch 12/100
900/900 [==============================] - 0s 196us/sample - loss: 0.4089 - mape: 206.0919
Epoch 13/100
900/900 [==============================] - 0s 193us/sample - loss: 0.3707 - mape: 230.9236
Epoch 14/100
900/900 [==============================] - 0s 186us/sample - loss: 0.3410 - mape: 248.6063
Epoch 15/100
900/900 [==============================] - 0s 192us/sample - loss: 0.3208 - mape: 258.0475
Epoch 16/100
900/900 [==============================] - 0s 187us/sample - loss: 0.3083 - mape: 276.5189
Epoch 17/100
900/900 [==============================] - 0s 177us/sample - loss: 0.2968 - mape: 287.3281
Epoch 18/100
900/900 [==============================] - 0s 183us/sample - loss: 0.2902 - mape: 304.3135
Epoch 19/100
900/900 [==============================] - 0s 189us/sample - loss: 0.2842 - mape: 292.3876
Epoch 20/100
900/900 [==============================] - 0s 185us/sample - loss: 0.2802 - mape: 294.3054
Epoch 21/100
900/900 [==============================] - 0s 198us/sample - loss: 0.2762 - mape: 295.0771
Epoch 22/100
900/900 [==============================] - 0s 188us/sample - loss: 0.2723 - mape: 301.5387
Epoch 23/100
900/900 [==============================] - 0s 186us/sample - loss: 0.2673 - mape: 296.3002
Epoch 24/100
900/900 [==============================] - 0s 185us/sample - loss: 0.2655 - mape: 286.4019
Epoch 25/100
900/900 [==============================] - 0s 198us/sample - loss: 0.2650 - mape: 295.7499
Epoch 26/100
900/900 [==============================] - 0s 187us/sample - loss: 0.2597 - mape: 296.9456
Epoch 27/100
900/900 [==============================] - 0s 199us/sample - loss: 0.2601 - mape: 279.2125
Epoch 28/100
900/900 [==============================] - 0s 198us/sample - loss: 0.2533 - mape: 288.8799
Epoch 29/100
900/900 [==============================] - 0s 201us/sample - loss: 0.2469 - mape: 287.6161
Epoch 30/100
900/900 [==============================] - 0s 179us/sample - loss: 0.2436 - mape: 271.4766
Epoch 31/100
900/900 [==============================] - 0s 187us/sample - loss: 0.2377 - mape: 272.0242
Epoch 32/100
900/900 [==============================] - 0s 182us/sample - loss: 0.2349 - mape: 269.1444
Epoch 33/100
900/900 [==============================] - 0s 181us/sample - loss: 0.2317 - mape: 270.4389
Epoch 34/100
900/900 [==============================] - 0s 184us/sample - loss: 0.2291 - mape: 259.8926
Epoch 35/100
900/900 [==============================] - 0s 183us/sample - loss: 0.2267 - mape: 255.4664
Epoch 36/100
900/900 [==============================] - 0s 189us/sample - loss: 0.2239 - mape: 261.1479
Epoch 37/100
900/900 [==============================] - 0s 209us/sample - loss: 0.2250 - mape: 266.7558
Epoch 38/100
900/900 [==============================] - 0s 217us/sample - loss: 0.2205 - mape: 264.2413
Epoch 39/100
900/900 [==============================] - 0s 183us/sample - loss: 0.2184 - mape: 262.9426
Epoch 40/100
900/900 [==============================] - 0s 223us/sample - loss: 0.2140 - mape: 258.2297
Epoch 41/100
900/900 [==============================] - 0s 288us/sample - loss: 0.2117 - mape: 252.5215
Epoch 42/100
900/900 [==============================] - 0s 221us/sample - loss: 0.2106 - mape: 250.6110
Epoch 43/100
900/900 [==============================] - 0s 248us/sample - loss: 0.2076 - mape: 253.7611
Epoch 44/100
900/900 [==============================] - 0s 233us/sample - loss: 0.2073 - mape: 256.7436
Epoch 45/100
900/900 [==============================] - 0s 205us/sample - loss: 0.2053 - mape: 243.5077
Epoch 46/100
900/900 [==============================] - 0s 267us/sample - loss: 0.2019 - mape: 243.2053
Epoch 47/100
900/900 [==============================] - 0s 215us/sample - loss: 0.2019 - mape: 248.5658
Epoch 48/100
900/900 [==============================] - 0s 238us/sample - loss: 0.1990 - mape: 239.8024
Epoch 49/100
900/900 [==============================] - 0s 238us/sample - loss: 0.1977 - mape: 240.7237
Epoch 50/100
900/900 [==============================] - 0s 201us/sample - loss: 0.1963 - mape: 228.0875
Epoch 51/100
900/900 [==============================] - 0s 186us/sample - loss: 0.1986 - mape: 246.7753
Epoch 52/100
900/900 [==============================] - 0s 212us/sample - loss: 0.1927 - mape: 237.7096
Epoch 53/100
900/900 [==============================] - 0s 177us/sample - loss: 0.1915 - mape: 237.9841
Epoch 54/100
900/900 [==============================] - 0s 194us/sample - loss: 0.1912 - mape: 227.1314
Epoch 55/100
900/900 [==============================] - 0s 173us/sample - loss: 0.1925 - mape: 232.2966
Epoch 56/100
900/900 [==============================] - 0s 295us/sample - loss: 0.1898 - mape: 236.6484
Epoch 57/100
900/900 [==============================] - 0s 197us/sample - loss: 0.1880 - mape: 236.1455
Epoch 58/100
900/900 [==============================] - 0s 272us/sample - loss: 0.1888 - mape: 237.5421
Epoch 59/100
900/900 [==============================] - 0s 222us/sample - loss: 0.1874 - mape: 240.4873
Epoch 60/100
900/900 [==============================] - 0s 257us/sample - loss: 0.1879 - mape: 226.1057
Epoch 61/100
900/900 [==============================] - 0s 253us/sample - loss: 0.1854 - mape: 227.4727
Epoch 62/100
900/900 [==============================] - 0s 194us/sample - loss: 0.1874 - mape: 225.6724
Epoch 63/100
900/900 [==============================] - 0s 175us/sample - loss: 0.1834 - mape: 227.1784
Epoch 64/100
900/900 [==============================] - 0s 221us/sample - loss: 0.1835 - mape: 230.1133
Epoch 65/100
900/900 [==============================] - 0s 262us/sample - loss: 0.1825 - mape: 222.8086
Epoch 66/100
900/900 [==============================] - 0s 236us/sample - loss: 0.1814 - mape: 217.2498
Epoch 67/100
900/900 [==============================] - 0s 223us/sample - loss: 0.1833 - mape: 226.5325
Epoch 68/100
900/900 [==============================] - 0s 240us/sample - loss: 0.1808 - mape: 215.9711
Epoch 69/100
900/900 [==============================] - 0s 228us/sample - loss: 0.1805 - mape: 222.7063
Epoch 70/100
900/900 [==============================] - 0s 197us/sample - loss: 0.1803 - mape: 220.8909
Epoch 71/100
900/900 [==============================] - 0s 201us/sample - loss: 0.1791 - mape: 218.5792
Epoch 72/100
900/900 [==============================] - 0s 281us/sample - loss: 0.1784 - mape: 215.1091
Epoch 73/100
900/900 [==============================] - 0s 248us/sample - loss: 0.1789 - mape: 213.6558
Epoch 74/100
900/900 [==============================] - 0s 213us/sample - loss: 0.1782 - mape: 217.4238
Epoch 75/100
900/900 [==============================] - 0s 266us/sample - loss: 0.1827 - mape: 243.4462
Epoch 76/100
900/900 [==============================] - 0s 180us/sample - loss: 0.1766 - mape: 217.2935
Epoch 77/100
900/900 [==============================] - 0s 177us/sample - loss: 0.1764 - mape: 216.2845
Epoch 78/100
900/900 [==============================] - 0s 168us/sample - loss: 0.1773 - mape: 213.9372
Epoch 79/100
900/900 [==============================] - 0s 223us/sample - loss: 0.1755 - mape: 218.9747
Epoch 80/100
900/900 [==============================] - 0s 183us/sample - loss: 0.1774 - mape: 218.7054
Epoch 81/100
900/900 [==============================] - 0s 183us/sample - loss: 0.1769 - mape: 212.9746
Epoch 82/100
900/900 [==============================] - 0s 180us/sample - loss: 0.1759 - mape: 216.8962
Epoch 83/100
900/900 [==============================] - 0s 174us/sample - loss: 0.1749 - mape: 222.6659
Epoch 84/100
900/900 [==============================] - 0s 175us/sample - loss: 0.1752 - mape: 205.6387
Epoch 85/100
900/900 [==============================] - 0s 174us/sample - loss: 0.1743 - mape: 211.9823
Epoch 86/100
900/900 [==============================] - 0s 174us/sample - loss: 0.1737 - mape: 219.6483
Epoch 87/100
900/900 [==============================] - 0s 177us/sample - loss: 0.1732 - mape: 217.6315
Epoch 88/100
900/900 [==============================] - 0s 183us/sample - loss: 0.1741 - mape: 217.9983
Epoch 89/100
900/900 [==============================] - 0s 166us/sample - loss: 0.1745 - mape: 206.2586
Epoch 90/100
900/900 [==============================] - 0s 177us/sample - loss: 0.1731 - mape: 222.3474
Epoch 91/100
900/900 [==============================] - 0s 183us/sample - loss: 0.1720 - mape: 212.1967
Epoch 92/100
900/900 [==============================] - 0s 166us/sample - loss: 0.1744 - mape: 215.4123
Epoch 93/100
900/900 [==============================] - 0s 175us/sample - loss: 0.1752 - mape: 221.3754
Epoch 94/100
900/900 [==============================] - 0s 209us/sample - loss: 0.1727 - mape: 222.7617
Epoch 95/100
900/900 [==============================] - 0s 306us/sample - loss: 0.1719 - mape: 209.2482
Epoch 96/100
900/900 [==============================] - 0s 291us/sample - loss: 0.1716 - mape: 202.8925
Epoch 97/100
900/900 [==============================] - 0s 268us/sample - loss: 0.1711 - mape: 220.4949
Epoch 98/100
900/900 [==============================] - 0s 212us/sample - loss: 0.1705 - mape: 212.8038
Epoch 99/100
900/900 [==============================] - 0s 265us/sample - loss: 0.1736 - mape: 225.6118
Epoch 100/100
900/900 [==============================] - 0s 185us/sample - loss: 0.1723 - mape: 221.9312

TSNE降维

tsne = TSNE(method='exact',init='pca',n_components=2, random_state=0, perplexity=5)
#learning rate:学习率，表示梯度下降的快慢，默认为200，建议取值在10到1000之间
#n_iter:迭代次数，默认为1000，自定义设置时应保证大于250
#perplexity:混乱度，表示t-SNE优化过程中考虑邻近点的多少，默认为30，建议取值在5到50之间
#method:两种优化方法：barnets_hut和exact。第一种耗时O(NlogN)，第二种耗时O(N^2)但是误差小
#metric:表示向量间距离度量的方式，默认是欧氏距离。如果是precomputed，则输入X是计算好的距离矩阵。也可以是自定义的距离度量函数。
#init:初始化，默认为random。取值为random为随机初始化，取值为pca为利用PCA进行初始化（常用），取值为numpy数组时必须shape=(n_samples, n_components)
tsne_data = tsne.fit_transform(data_normal)

tsne_data.shape,data_normal.shape

((937, 2), (937, 3))

plt.figure(figsize=(12,8))
plt.title('TSNE Components')
plt.scatter(tsne_data[:,0], tsne_data[:,1])

在这里插入图片描述

训练简单神经网络

tsne_train_data=tsne_data[:900]
tsne_test_data=tsne_data[901:]
tsne_model=tf.keras.Sequential([tf.keras.layers.Dense(10,input_shape=(2,),activation='relu'),
                           tf.keras.layers.Dense(10,activation='relu'),
                           tf.keras.layers.Dense(1)])
tsne_model.summary()#查看模型基本信息
tsne_model.compile(optimizer='adam',loss='mse',metrics=['mape']) #设置优化器和损失函数
tsne_history=tsne_model.fit(tsne_train_data,train_label,epochs=100) #每个数据训练1000次

Model: "sequential_2"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
dense_6 (Dense)              (None, 10)                30        
_________________________________________________________________
dense_7 (Dense)              (None, 10)                110       
_________________________________________________________________
dense_8 (Dense)              (None, 1)                 11        
=================================================================
Total params: 151
Trainable params: 151
Non-trainable params: 0
_________________________________________________________________
Train on 900 samples
Epoch 1/100
900/900 [==============================] - 1s 2ms/sample - loss: 89.3856 - mape: 6934.5850
Epoch 2/100
900/900 [==============================] - 0s 212us/sample - loss: 21.7487 - mape: 3360.4509
Epoch 3/100
900/900 [==============================] - 0s 272us/sample - loss: 7.2081 - mape: 2058.5820
Epoch 4/100
900/900 [==============================] - 0s 267us/sample - loss: 3.9222 - mape: 1663.4132
Epoch 5/100
900/900 [==============================] - 0s 230us/sample - loss: 2.7726 - mape: 1321.7251
Epoch 6/100
900/900 [==============================] - 0s 248us/sample - loss: 2.2014 - mape: 1145.6675
Epoch 7/100
900/900 [==============================] - 0s 201us/sample - loss: 1.8586 - mape: 1012.2946
Epoch 8/100
900/900 [==============================] - 0s 213us/sample - loss: 1.6085 - mape: 903.4532
Epoch 9/100
900/900 [==============================] - 0s 303us/sample - loss: 1.3883 - mape: 829.0187
Epoch 10/100
900/900 [==============================] - 0s 208us/sample - loss: 1.2411 - mape: 798.7681
Epoch 11/100
900/900 [==============================] - 0s 195us/sample - loss: 1.1060 - mape: 750.3655
Epoch 12/100
900/900 [==============================] - 0s 271us/sample - loss: 1.0106 - mape: 696.5897
Epoch 13/100
900/900 [==============================] - 0s 234us/sample - loss: 0.9510 - mape: 650.1858
Epoch 14/100
900/900 [==============================] - 0s 183us/sample - loss: 0.8703 - mape: 652.8735
Epoch 15/100
900/900 [==============================] - 0s 227us/sample - loss: 0.8276 - mape: 627.7747
Epoch 16/100
900/900 [==============================] - 0s 205us/sample - loss: 0.7819 - mape: 591.9622
Epoch 17/100
900/900 [==============================] - 0s 196us/sample - loss: 0.7341 - mape: 603.2118 - loss: 0.7363 - mape: 605.45
Epoch 18/100
900/900 [==============================] - 0s 201us/sample - loss: 0.6928 - mape: 566.8422
Epoch 19/100
900/900 [==============================] - 0s 194us/sample - loss: 0.6713 - mape: 585.1208
Epoch 20/100
900/900 [==============================] - 0s 205us/sample - loss: 0.6247 - mape: 549.8159
Epoch 21/100
900/900 [==============================] - 0s 185us/sample - loss: 0.5897 - mape: 532.0855
Epoch 22/100
900/900 [==============================] - 0s 192us/sample - loss: 0.5658 - mape: 527.9331
Epoch 23/100
900/900 [==============================] - 0s 187us/sample - loss: 0.5446 - mape: 488.4539
Epoch 24/100
900/900 [==============================] - 0s 175us/sample - loss: 0.5169 - mape: 468.9429
Epoch 25/100
900/900 [==============================] - 0s 207us/sample - loss: 0.5201 - mape: 467.5760
Epoch 26/100
900/900 [==============================] - 0s 304us/sample - loss: 0.4877 - mape: 452.7673
Epoch 27/100
900/900 [==============================] - 0s 197us/sample - loss: 0.4782 - mape: 440.4234
Epoch 28/100
900/900 [==============================] - 0s 319us/sample - loss: 0.4719 - mape: 439.3805
Epoch 29/100
900/900 [==============================] - 0s 213us/sample - loss: 0.4486 - mape: 405.8721
Epoch 30/100
900/900 [==============================] - ETA: 0s - loss: 0.4335 - mape: 432.51 - 0s 327us/sample - loss: 0.4355 - mape: 420.7079
Epoch 31/100
900/900 [==============================] - 0s 202us/sample - loss: 0.4259 - mape: 408.2603
Epoch 32/100
900/900 [==============================] - 0s 212us/sample - loss: 0.4334 - mape: 410.5372
Epoch 33/100
900/900 [==============================] - 0s 213us/sample - loss: 0.4127 - mape: 385.4236
Epoch 34/100
900/900 [==============================] - 0s 269us/sample - loss: 0.3966 - mape: 402.8446
Epoch 35/100
900/900 [==============================] - 0s 199us/sample - loss: 0.3908 - mape: 410.7667
Epoch 36/100
900/900 [==============================] - 0s 204us/sample - loss: 0.3764 - mape: 368.6591
Epoch 37/100
900/900 [==============================] - 0s 191us/sample - loss: 0.3710 - mape: 371.9299
Epoch 38/100
900/900 [==============================] - 0s 279us/sample - loss: 0.3618 - mape: 362.1169
Epoch 39/100
900/900 [==============================] - 0s 229us/sample - loss: 0.3583 - mape: 383.2825
Epoch 40/100
900/900 [==============================] - 0s 194us/sample - loss: 0.3447 - mape: 360.1974
Epoch 41/100
900/900 [==============================] - 0s 186us/sample - loss: 0.3384 - mape: 354.0529
Epoch 42/100
900/900 [==============================] - 0s 205us/sample - loss: 0.3303 - mape: 353.8742
Epoch 43/100
900/900 [==============================] - 0s 246us/sample - loss: 0.3518 - mape: 376.3791
Epoch 44/100
900/900 [==============================] - 0s 311us/sample - loss: 0.3161 - mape: 353.5660
Epoch 45/100
900/900 [==============================] - 0s 239us/sample - loss: 0.3059 - mape: 345.9633
Epoch 46/100
900/900 [==============================] - 0s 219us/sample - loss: 0.3012 - mape: 335.1208
Epoch 47/100
900/900 [==============================] - 0s 257us/sample - loss: 0.2983 - mape: 340.1209
Epoch 48/100
900/900 [==============================] - 0s 195us/sample - loss: 0.2884 - mape: 318.1566
Epoch 49/100
900/900 [==============================] - 0s 235us/sample - loss: 0.2840 - mape: 331.5550
Epoch 50/100
900/900 [==============================] - 0s 194us/sample - loss: 0.2825 - mape: 344.0093
Epoch 51/100
900/900 [==============================] - 0s 239us/sample - loss: 0.2776 - mape: 320.8491
Epoch 52/100
900/900 [==============================] - 0s 225us/sample - loss: 0.2791 - mape: 312.0119
Epoch 53/100
900/900 [==============================] - 0s 209us/sample - loss: 0.2723 - mape: 325.9967
Epoch 54/100
900/900 [==============================] - 0s 283us/sample - loss: 0.2650 - mape: 299.1724
Epoch 55/100
900/900 [==============================] - 0s 249us/sample - loss: 0.2611 - mape: 287.6593
Epoch 56/100
900/900 [==============================] - 0s 260us/sample - loss: 0.2663 - mape: 304.5265
Epoch 57/100
900/900 [==============================] - 0s 224us/sample - loss: 0.2570 - mape: 288.9542 - loss: 0.2604 - mape: 365.
Epoch 58/100
900/900 [==============================] - 0s 239us/sample - loss: 0.2542 - mape: 300.7240
Epoch 59/100
900/900 [==============================] - 0s 219us/sample - loss: 0.2481 - mape: 287.3276
Epoch 60/100
900/900 [==============================] - 0s 219us/sample - loss: 0.2510 - mape: 305.7006
Epoch 61/100
900/900 [==============================] - 0s 226us/sample - loss: 0.2462 - mape: 315.4265
Epoch 62/100
900/900 [==============================] - 0s 275us/sample - loss: 0.2400 - mape: 315.1567
Epoch 63/100
900/900 [==============================] - 0s 235us/sample - loss: 0.2456 - mape: 271.3877
Epoch 64/100
900/900 [==============================] - 0s 217us/sample - loss: 0.2428 - mape: 310.5604
Epoch 65/100
900/900 [==============================] - 0s 222us/sample - loss: 0.2348 - mape: 304.9576
Epoch 66/100
900/900 [==============================] - 0s 186us/sample - loss: 0.2274 - mape: 277.8430
Epoch 67/100
900/900 [==============================] - 0s 201us/sample - loss: 0.2266 - mape: 273.0795
Epoch 68/100
900/900 [==============================] - 0s 187us/sample - loss: 0.2298 - mape: 289.7397
Epoch 69/100
900/900 [==============================] - 0s 215us/sample - loss: 0.2250 - mape: 276.7233
Epoch 70/100
900/900 [==============================] - 0s 195us/sample - loss: 0.2203 - mape: 269.2948
Epoch 71/100
900/900 [==============================] - 0s 229us/sample - loss: 0.2177 - mape: 256.3293
Epoch 72/100
900/900 [==============================] - 0s 177us/sample - loss: 0.2180 - mape: 263.3174
Epoch 73/100
900/900 [==============================] - 0s 173us/sample - loss: 0.2400 - mape: 295.4164
Epoch 74/100
900/900 [==============================] - 0s 177us/sample - loss: 0.2255 - mape: 279.1955
Epoch 75/100
900/900 [==============================] - 0s 191us/sample - loss: 0.2176 - mape: 274.6373
Epoch 76/100
900/900 [==============================] - 0s 172us/sample - loss: 0.2157 - mape: 292.2338
Epoch 77/100
900/900 [==============================] - 0s 171us/sample - loss: 0.2119 - mape: 264.9478
Epoch 78/100
900/900 [==============================] - 0s 176us/sample - loss: 0.2127 - mape: 269.1001
Epoch 79/100
900/900 [==============================] - 0s 195us/sample - loss: 0.2127 - mape: 281.3460
Epoch 80/100
900/900 [==============================] - 0s 180us/sample - loss: 0.2055 - mape: 276.5002
Epoch 81/100
900/900 [==============================] - 0s 238us/sample - loss: 0.2062 - mape: 279.3807
Epoch 82/100
900/900 [==============================] - 0s 166us/sample - loss: 0.2129 - mape: 290.9121
Epoch 83/100
900/900 [==============================] - 0s 162us/sample - loss: 0.2110 - mape: 290.7337
Epoch 84/100
900/900 [==============================] - 0s 176us/sample - loss: 0.2045 - mape: 272.8240
Epoch 85/100
900/900 [==============================] - 0s 175us/sample - loss: 0.2034 - mape: 296.3637
Epoch 86/100
900/900 [==============================] - 0s 176us/sample - loss: 0.1994 - mape: 261.6485
Epoch 87/100
900/900 [==============================] - 0s 180us/sample - loss: 0.1970 - mape: 289.5106
Epoch 88/100
900/900 [==============================] - 0s 182us/sample - loss: 0.1983 - mape: 287.4521
Epoch 89/100
900/900 [==============================] - 0s 178us/sample - loss: 0.2053 - mape: 290.1107
Epoch 90/100
900/900 [==============================] - 0s 174us/sample - loss: 0.1981 - mape: 307.7013
Epoch 91/100
900/900 [==============================] - 0s 232us/sample - loss: 0.2053 - mape: 299.0280
Epoch 92/100
900/900 [==============================] - 0s 171us/sample - loss: 0.2096 - mape: 316.3831
Epoch 93/100
900/900 [==============================] - 0s 182us/sample - loss: 0.2034 - mape: 303.2004
Epoch 94/100
900/900 [==============================] - 0s 188us/sample - loss: 0.2196 - mape: 262.1139
Epoch 95/100
900/900 [==============================] - 0s 193us/sample - loss: 0.1936 - mape: 325.1552
Epoch 96/100
900/900 [==============================] - 0s 197us/sample - loss: 0.1936 - mape: 303.9213
Epoch 97/100
900/900 [==============================] - 0s 181us/sample - loss: 0.1921 - mape: 288.6425
Epoch 98/100
900/900 [==============================] - 0s 168us/sample - loss: 0.1920 - mape: 313.0998
Epoch 99/100
900/900 [==============================] - 0s 173us/sample - loss: 0.1954 - mape: 305.5129
Epoch 100/100
900/900 [==============================] - 0s 172us/sample - loss: 0.1889 - mape: 314.0016

对比评估

#模型评估
pca_model.evaluate(pca_test_data,test_label)
ica_model.evaluate(ica_test_data,test_label)
tsne_model.evaluate(tsne_test_data,test_label)

36/1 [========================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================] - 0s 8ms/sample - loss: 0.6366 - mape: 146.9983
36/1 [========================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================] - 0s 4ms/sample - loss: 0.6447 - mape: 155.4730
36/1 [========================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================] - 0s 4ms/sample - loss: 0.2164 - mape: 292.4103





[0.3441612786716885, 292.4103]

plt.figure(figsize=(10,5))
pca_history.history.keys() #查看history中存储了哪些参数
plt.subplot(1,3,1)
plt.title("PCA")
plt.plot(pca_history.epoch,pca_history.history.get('loss')) #画出随着epoch增大loss的变化图
ica_history.history.keys() #查看history中存储了哪些参数
plt.subplot(1,3,2)
plt.title("ICA")
plt.plot(ica_history.epoch,ica_history.history.get('loss')) #画出随着epoch增大loss的变化图
plt.subplot(1,3,3)
plt.title("TSNE")
plt.plot(tsne_history.epoch,tsne_history.history.get('loss')) #画出随着epoch增大loss的变化图
plt.show()

在这里插入图片描述