-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy path全部代码(可直接复制运行).py
80 lines (65 loc) · 18.4 KB
/
全部代码(可直接复制运行).py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
# ================基于XGBoost原生接口的分类=============
import numpy as np
from sklearn.datasets import load_iris
import xgboost as xgb
from xgboost import plot_importance
from matplotlib import pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score # 准确率
# 加载样本数据集
# iris = load_iris()
# X,y = iris.data,iris.target
import pandas as pd
path = 'fashion-mnist_train.csv'
path1 = 'fashion-mnist_test_data.csv'
# 使用pandas读入
data = pd.read_csv(path) #读取文件中所有数据
# 按列分离数据
X = data[['pixel1','pixel2','pixel3','pixel4','pixel5','pixel6','pixel7','pixel8','pixel9','pixel10','pixel11','pixel12','pixel13','pixel14','pixel15','pixel16','pixel17','pixel18','pixel19','pixel20','pixel21','pixel22','pixel23','pixel24','pixel25','pixel26','pixel27','pixel28','pixel29','pixel30','pixel31','pixel32','pixel33','pixel34','pixel35','pixel36','pixel37','pixel38','pixel39','pixel40','pixel41','pixel42','pixel43','pixel44','pixel45','pixel46','pixel47','pixel48','pixel49','pixel50','pixel51','pixel52','pixel53','pixel54','pixel55','pixel56','pixel57','pixel58','pixel59','pixel60','pixel61','pixel62','pixel63','pixel64','pixel65','pixel66','pixel67','pixel68','pixel69','pixel70','pixel71','pixel72','pixel73','pixel74','pixel75','pixel76','pixel77','pixel78','pixel79','pixel80','pixel81','pixel82','pixel83','pixel84','pixel85','pixel86','pixel87','pixel88','pixel89','pixel90','pixel91','pixel92','pixel93','pixel94','pixel95','pixel96','pixel97','pixel98','pixel99','pixel100','pixel101','pixel102','pixel103','pixel104','pixel105','pixel106','pixel107','pixel108','pixel109','pixel110','pixel111','pixel112','pixel113','pixel114','pixel115','pixel116','pixel117','pixel118','pixel119','pixel120','pixel121','pixel122','pixel123','pixel124','pixel125','pixel126','pixel127','pixel128','pixel129','pixel130','pixel131','pixel132','pixel133','pixel134','pixel135','pixel136','pixel137','pixel138','pixel139','pixel140','pixel141','pixel142','pixel143','pixel144','pixel145','pixel146','pixel147','pixel148','pixel149','pixel150','pixel151','pixel152','pixel153','pixel154','pixel155','pixel156','pixel157','pixel158','pixel159','pixel160','pixel161','pixel162','pixel163','pixel164','pixel165','pixel166','pixel167','pixel168','pixel169','pixel170','pixel171','pixel172','pixel173','pixel174','pixel175','pixel176','pixel177','pixel178','pixel179','pixel180','pixel181','pixel182','pixel183','pixel184','pixel185','pixel186','pixel187','pixel188','pixel189','pixel190','pixel191','pixel192','pixel193','pixel194','pixel195','pixel196','pixel197','pixel198','pixel199','pixel200','pixel201','pixel202','pixel203','pixel204','pixel205','pixel206','pixel207','pixel208','pixel209','pixel210','pixel211','pixel212','pixel213','pixel214','pixel215','pixel216','pixel217','pixel218','pixel219','pixel220','pixel221','pixel222','pixel223','pixel224','pixel225','pixel226','pixel227','pixel228','pixel229','pixel230','pixel231','pixel232','pixel233','pixel234','pixel235','pixel236','pixel237','pixel238','pixel239','pixel240','pixel241','pixel242','pixel243','pixel244','pixel245','pixel246','pixel247','pixel248','pixel249','pixel250','pixel251','pixel252','pixel253','pixel254','pixel255','pixel256','pixel257','pixel258','pixel259','pixel260','pixel261','pixel262','pixel263','pixel264','pixel265','pixel266','pixel267','pixel268','pixel269','pixel270','pixel271','pixel272','pixel273','pixel274','pixel275','pixel276','pixel277','pixel278','pixel279','pixel280','pixel281','pixel282','pixel283','pixel284','pixel285','pixel286','pixel287','pixel288','pixel289','pixel290','pixel291','pixel292','pixel293','pixel294','pixel295','pixel296','pixel297','pixel298','pixel299','pixel300','pixel301','pixel302','pixel303','pixel304','pixel305','pixel306','pixel307','pixel308','pixel309','pixel310','pixel311','pixel312','pixel313','pixel314','pixel315','pixel316','pixel317','pixel318','pixel319','pixel320','pixel321','pixel322','pixel323','pixel324','pixel325','pixel326','pixel327','pixel328','pixel329','pixel330','pixel331','pixel332','pixel333','pixel334','pixel335','pixel336','pixel337','pixel338','pixel339','pixel340','pixel341','pixel342','pixel343','pixel344','pixel345','pixel346','pixel347','pixel348','pixel349','pixel350','pixel351','pixel352','pixel353','pixel354','pixel355','pixel356','pixel357','pixel358','pixel359','pixel360','pixel361','pixel362','pixel363','pixel364','pixel365','pixel366','pixel367','pixel368','pixel369','pixel370','pixel371','pixel372','pixel373','pixel374','pixel375','pixel376','pixel377','pixel378','pixel379','pixel380','pixel381','pixel382','pixel383','pixel384','pixel385','pixel386','pixel387','pixel388','pixel389','pixel390','pixel391','pixel392','pixel393','pixel394','pixel395','pixel396','pixel397','pixel398','pixel399','pixel400','pixel401','pixel402','pixel403','pixel404','pixel405','pixel406','pixel407','pixel408','pixel409','pixel410','pixel411','pixel412','pixel413','pixel414','pixel415','pixel416','pixel417','pixel418','pixel419','pixel420','pixel421','pixel422','pixel423','pixel424','pixel425','pixel426','pixel427','pixel428','pixel429','pixel430','pixel431','pixel432','pixel433','pixel434','pixel435','pixel436','pixel437','pixel438','pixel439','pixel440','pixel441','pixel442','pixel443','pixel444','pixel445','pixel446','pixel447','pixel448','pixel449','pixel450','pixel451','pixel452','pixel453','pixel454','pixel455','pixel456','pixel457','pixel458','pixel459','pixel460','pixel461','pixel462','pixel463','pixel464','pixel465','pixel466','pixel467','pixel468','pixel469','pixel470','pixel471','pixel472','pixel473','pixel474','pixel475','pixel476','pixel477','pixel478','pixel479','pixel480','pixel481','pixel482','pixel483','pixel484','pixel485','pixel486','pixel487','pixel488','pixel489','pixel490','pixel491','pixel492','pixel493','pixel494','pixel495','pixel496','pixel497','pixel498','pixel499','pixel500','pixel501','pixel502','pixel503','pixel504','pixel505','pixel506','pixel507','pixel508','pixel509','pixel510','pixel511','pixel512','pixel513','pixel514','pixel515','pixel516','pixel517','pixel518','pixel519','pixel520','pixel521','pixel522','pixel523','pixel524','pixel525','pixel526','pixel527','pixel528','pixel529','pixel530','pixel531','pixel532','pixel533','pixel534','pixel535','pixel536','pixel537','pixel538','pixel539','pixel540','pixel541','pixel542','pixel543','pixel544','pixel545','pixel546','pixel547','pixel548','pixel549','pixel550','pixel551','pixel552','pixel553','pixel554','pixel555','pixel556','pixel557','pixel558','pixel559','pixel560','pixel561','pixel562','pixel563','pixel564','pixel565','pixel566','pixel567','pixel568','pixel569','pixel570','pixel571','pixel572','pixel573','pixel574','pixel575','pixel576','pixel577','pixel578','pixel579','pixel580','pixel581','pixel582','pixel583','pixel584','pixel585','pixel586','pixel587','pixel588','pixel589','pixel590','pixel591','pixel592','pixel593','pixel594','pixel595','pixel596','pixel597','pixel598','pixel599','pixel600','pixel601','pixel602','pixel603','pixel604','pixel605','pixel606','pixel607','pixel608','pixel609','pixel610','pixel611','pixel612','pixel613','pixel614','pixel615','pixel616','pixel617','pixel618','pixel619','pixel620','pixel621','pixel622','pixel623','pixel624','pixel625','pixel626','pixel627','pixel628','pixel629','pixel630','pixel631','pixel632','pixel633','pixel634','pixel635','pixel636','pixel637','pixel638','pixel639','pixel640','pixel641','pixel642','pixel643','pixel644','pixel645','pixel646','pixel647','pixel648','pixel649','pixel650','pixel651','pixel652','pixel653','pixel654','pixel655','pixel656','pixel657','pixel658','pixel659','pixel660','pixel661','pixel662','pixel663','pixel664','pixel665','pixel666','pixel667','pixel668','pixel669','pixel670','pixel671','pixel672','pixel673','pixel674','pixel675','pixel676','pixel677','pixel678','pixel679','pixel680','pixel681','pixel682','pixel683','pixel684','pixel685','pixel686','pixel687','pixel688','pixel689','pixel690','pixel691','pixel692','pixel693','pixel694','pixel695','pixel696','pixel697','pixel698','pixel699','pixel700','pixel701','pixel702','pixel703','pixel704','pixel705','pixel706','pixel707','pixel708','pixel709','pixel710','pixel711','pixel712','pixel713','pixel714','pixel715','pixel716','pixel717','pixel718','pixel719','pixel720','pixel721','pixel722','pixel723','pixel724','pixel725','pixel726','pixel727','pixel728','pixel729','pixel730','pixel731','pixel732','pixel733','pixel734','pixel735','pixel736','pixel737','pixel738','pixel739','pixel740','pixel741','pixel742','pixel743','pixel744','pixel745','pixel746','pixel747','pixel748','pixel749','pixel750','pixel751','pixel752','pixel753','pixel754','pixel755','pixel756','pixel757','pixel758','pixel759','pixel760','pixel761','pixel762','pixel763','pixel764','pixel765','pixel766','pixel767','pixel768','pixel769','pixel770','pixel771','pixel772','pixel773','pixel774','pixel775','pixel776','pixel777','pixel778','pixel779','pixel780','pixel781','pixel782','pixel783','pixel784']]
y = data[['label']]
feature = np.array(X)
label = np.array(y)
print(feature)
print(label)
# X_train, X_test, y_train, y_test = train_test_split(feature, label, train_size=1, random_state=1234565) # 数据集分割
# 使用pandas读入
data = pd.read_csv(path1) #读取文件中所有数据
# 按列分离数据
X = data[['pixel1','pixel2','pixel3','pixel4','pixel5','pixel6','pixel7','pixel8','pixel9','pixel10','pixel11','pixel12','pixel13','pixel14','pixel15','pixel16','pixel17','pixel18','pixel19','pixel20','pixel21','pixel22','pixel23','pixel24','pixel25','pixel26','pixel27','pixel28','pixel29','pixel30','pixel31','pixel32','pixel33','pixel34','pixel35','pixel36','pixel37','pixel38','pixel39','pixel40','pixel41','pixel42','pixel43','pixel44','pixel45','pixel46','pixel47','pixel48','pixel49','pixel50','pixel51','pixel52','pixel53','pixel54','pixel55','pixel56','pixel57','pixel58','pixel59','pixel60','pixel61','pixel62','pixel63','pixel64','pixel65','pixel66','pixel67','pixel68','pixel69','pixel70','pixel71','pixel72','pixel73','pixel74','pixel75','pixel76','pixel77','pixel78','pixel79','pixel80','pixel81','pixel82','pixel83','pixel84','pixel85','pixel86','pixel87','pixel88','pixel89','pixel90','pixel91','pixel92','pixel93','pixel94','pixel95','pixel96','pixel97','pixel98','pixel99','pixel100','pixel101','pixel102','pixel103','pixel104','pixel105','pixel106','pixel107','pixel108','pixel109','pixel110','pixel111','pixel112','pixel113','pixel114','pixel115','pixel116','pixel117','pixel118','pixel119','pixel120','pixel121','pixel122','pixel123','pixel124','pixel125','pixel126','pixel127','pixel128','pixel129','pixel130','pixel131','pixel132','pixel133','pixel134','pixel135','pixel136','pixel137','pixel138','pixel139','pixel140','pixel141','pixel142','pixel143','pixel144','pixel145','pixel146','pixel147','pixel148','pixel149','pixel150','pixel151','pixel152','pixel153','pixel154','pixel155','pixel156','pixel157','pixel158','pixel159','pixel160','pixel161','pixel162','pixel163','pixel164','pixel165','pixel166','pixel167','pixel168','pixel169','pixel170','pixel171','pixel172','pixel173','pixel174','pixel175','pixel176','pixel177','pixel178','pixel179','pixel180','pixel181','pixel182','pixel183','pixel184','pixel185','pixel186','pixel187','pixel188','pixel189','pixel190','pixel191','pixel192','pixel193','pixel194','pixel195','pixel196','pixel197','pixel198','pixel199','pixel200','pixel201','pixel202','pixel203','pixel204','pixel205','pixel206','pixel207','pixel208','pixel209','pixel210','pixel211','pixel212','pixel213','pixel214','pixel215','pixel216','pixel217','pixel218','pixel219','pixel220','pixel221','pixel222','pixel223','pixel224','pixel225','pixel226','pixel227','pixel228','pixel229','pixel230','pixel231','pixel232','pixel233','pixel234','pixel235','pixel236','pixel237','pixel238','pixel239','pixel240','pixel241','pixel242','pixel243','pixel244','pixel245','pixel246','pixel247','pixel248','pixel249','pixel250','pixel251','pixel252','pixel253','pixel254','pixel255','pixel256','pixel257','pixel258','pixel259','pixel260','pixel261','pixel262','pixel263','pixel264','pixel265','pixel266','pixel267','pixel268','pixel269','pixel270','pixel271','pixel272','pixel273','pixel274','pixel275','pixel276','pixel277','pixel278','pixel279','pixel280','pixel281','pixel282','pixel283','pixel284','pixel285','pixel286','pixel287','pixel288','pixel289','pixel290','pixel291','pixel292','pixel293','pixel294','pixel295','pixel296','pixel297','pixel298','pixel299','pixel300','pixel301','pixel302','pixel303','pixel304','pixel305','pixel306','pixel307','pixel308','pixel309','pixel310','pixel311','pixel312','pixel313','pixel314','pixel315','pixel316','pixel317','pixel318','pixel319','pixel320','pixel321','pixel322','pixel323','pixel324','pixel325','pixel326','pixel327','pixel328','pixel329','pixel330','pixel331','pixel332','pixel333','pixel334','pixel335','pixel336','pixel337','pixel338','pixel339','pixel340','pixel341','pixel342','pixel343','pixel344','pixel345','pixel346','pixel347','pixel348','pixel349','pixel350','pixel351','pixel352','pixel353','pixel354','pixel355','pixel356','pixel357','pixel358','pixel359','pixel360','pixel361','pixel362','pixel363','pixel364','pixel365','pixel366','pixel367','pixel368','pixel369','pixel370','pixel371','pixel372','pixel373','pixel374','pixel375','pixel376','pixel377','pixel378','pixel379','pixel380','pixel381','pixel382','pixel383','pixel384','pixel385','pixel386','pixel387','pixel388','pixel389','pixel390','pixel391','pixel392','pixel393','pixel394','pixel395','pixel396','pixel397','pixel398','pixel399','pixel400','pixel401','pixel402','pixel403','pixel404','pixel405','pixel406','pixel407','pixel408','pixel409','pixel410','pixel411','pixel412','pixel413','pixel414','pixel415','pixel416','pixel417','pixel418','pixel419','pixel420','pixel421','pixel422','pixel423','pixel424','pixel425','pixel426','pixel427','pixel428','pixel429','pixel430','pixel431','pixel432','pixel433','pixel434','pixel435','pixel436','pixel437','pixel438','pixel439','pixel440','pixel441','pixel442','pixel443','pixel444','pixel445','pixel446','pixel447','pixel448','pixel449','pixel450','pixel451','pixel452','pixel453','pixel454','pixel455','pixel456','pixel457','pixel458','pixel459','pixel460','pixel461','pixel462','pixel463','pixel464','pixel465','pixel466','pixel467','pixel468','pixel469','pixel470','pixel471','pixel472','pixel473','pixel474','pixel475','pixel476','pixel477','pixel478','pixel479','pixel480','pixel481','pixel482','pixel483','pixel484','pixel485','pixel486','pixel487','pixel488','pixel489','pixel490','pixel491','pixel492','pixel493','pixel494','pixel495','pixel496','pixel497','pixel498','pixel499','pixel500','pixel501','pixel502','pixel503','pixel504','pixel505','pixel506','pixel507','pixel508','pixel509','pixel510','pixel511','pixel512','pixel513','pixel514','pixel515','pixel516','pixel517','pixel518','pixel519','pixel520','pixel521','pixel522','pixel523','pixel524','pixel525','pixel526','pixel527','pixel528','pixel529','pixel530','pixel531','pixel532','pixel533','pixel534','pixel535','pixel536','pixel537','pixel538','pixel539','pixel540','pixel541','pixel542','pixel543','pixel544','pixel545','pixel546','pixel547','pixel548','pixel549','pixel550','pixel551','pixel552','pixel553','pixel554','pixel555','pixel556','pixel557','pixel558','pixel559','pixel560','pixel561','pixel562','pixel563','pixel564','pixel565','pixel566','pixel567','pixel568','pixel569','pixel570','pixel571','pixel572','pixel573','pixel574','pixel575','pixel576','pixel577','pixel578','pixel579','pixel580','pixel581','pixel582','pixel583','pixel584','pixel585','pixel586','pixel587','pixel588','pixel589','pixel590','pixel591','pixel592','pixel593','pixel594','pixel595','pixel596','pixel597','pixel598','pixel599','pixel600','pixel601','pixel602','pixel603','pixel604','pixel605','pixel606','pixel607','pixel608','pixel609','pixel610','pixel611','pixel612','pixel613','pixel614','pixel615','pixel616','pixel617','pixel618','pixel619','pixel620','pixel621','pixel622','pixel623','pixel624','pixel625','pixel626','pixel627','pixel628','pixel629','pixel630','pixel631','pixel632','pixel633','pixel634','pixel635','pixel636','pixel637','pixel638','pixel639','pixel640','pixel641','pixel642','pixel643','pixel644','pixel645','pixel646','pixel647','pixel648','pixel649','pixel650','pixel651','pixel652','pixel653','pixel654','pixel655','pixel656','pixel657','pixel658','pixel659','pixel660','pixel661','pixel662','pixel663','pixel664','pixel665','pixel666','pixel667','pixel668','pixel669','pixel670','pixel671','pixel672','pixel673','pixel674','pixel675','pixel676','pixel677','pixel678','pixel679','pixel680','pixel681','pixel682','pixel683','pixel684','pixel685','pixel686','pixel687','pixel688','pixel689','pixel690','pixel691','pixel692','pixel693','pixel694','pixel695','pixel696','pixel697','pixel698','pixel699','pixel700','pixel701','pixel702','pixel703','pixel704','pixel705','pixel706','pixel707','pixel708','pixel709','pixel710','pixel711','pixel712','pixel713','pixel714','pixel715','pixel716','pixel717','pixel718','pixel719','pixel720','pixel721','pixel722','pixel723','pixel724','pixel725','pixel726','pixel727','pixel728','pixel729','pixel730','pixel731','pixel732','pixel733','pixel734','pixel735','pixel736','pixel737','pixel738','pixel739','pixel740','pixel741','pixel742','pixel743','pixel744','pixel745','pixel746','pixel747','pixel748','pixel749','pixel750','pixel751','pixel752','pixel753','pixel754','pixel755','pixel756','pixel757','pixel758','pixel759','pixel760','pixel761','pixel762','pixel763','pixel764','pixel765','pixel766','pixel767','pixel768','pixel769','pixel770','pixel771','pixel772','pixel773','pixel774','pixel775','pixel776','pixel777','pixel778','pixel779','pixel780','pixel781','pixel782','pixel783','pixel784']]
test_feature = np.array(X)
print(test_feature)
# 算法参数
params = {
'booster': 'gbtree',
'objective': 'multi:softmax',
'num_class': 10,
'gamma': 0.1,
'max_depth': 6,
'lambda': 2,
'subsample': 0.7,
'colsample_bytree': 0.7,
'min_child_weight': 3,
'silent': 1,
'eta': 0.1,
'seed': 1000,
'nthread': 4,
}
plst = params.items()
print(plst)
dtrain = xgb.DMatrix(feature, label) # 生成数据集格式
num_rounds = 500
print(dtrain)
model = xgb.train(params, dtrain, num_rounds) # xgboost模型训练
#
# 对测试集进行预测
dtest = xgb.DMatrix(test_feature)
print(dtest)
y_pred = model.predict(dtest)
np.savetxt('data.txt',y_pred,fmt='%d')
print(y_pred)
# 计算准确率
# accuracy = accuracy_score(y_test,y_pred)
# print("accuarcy: %.2f%%" % (accuracy*100.0))
# 显示重要特征
plot_importance(model)
plt.show()