-
Notifications
You must be signed in to change notification settings - Fork 1
/
Copy pathtrain.py
197 lines (183 loc) · 6.64 KB
/
train.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
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
import pandas as pd
from sklearn.decomposition import PCA
from sklearn import decomposition
from sklearn.metrics import mean_squared_error, explained_variance_score, mean_absolute_error
import sklearn.externals.joblib as joblib
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.datasets import make_moons, make_circles, make_classification
from sklearn.neural_network import MLPClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.gaussian_process import GaussianProcessClassifier
from sklearn.gaussian_process.kernels import RBF
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier
from sklearn.naive_bayes import GaussianNB
#读取数据
LoanStats3a=pd.read_csv('processed_train_dataset17k.csv', nrows=17000)
# print(LoanStats3a.columns.values.tolist())
X, y = LoanStats3a[['in_len', 'number', 'vout_num_origin', 'value_origin', 'out_len', 'out_value', 'out_number', 'is_coinbase']], LoanStats3a[['label']]
print(X.shape, y.shape)
X = np.array(X)
Y = np.array(y)
Y=Y.ravel()
#查看特征的维度
print('提取后的矩阵维度',X.shape)
sc = StandardScaler()
X_std = sc.fit_transform(X)
joblib.dump(sc, 'standarsc')
#查看标准化后的特征数据
print('standardization\n',X_std)
# 创建PCA对象，n_components为主成分维度——列
# pca = decomposition.PCA(n_components=1)
# # #使用PCA对特征进行降维
# xGeoPCA = pca.fit_transform(X_std)
X_train, X_test, y_train, y_test = train_test_split(
X_std, Y, test_size=0.3, random_state=23375)
print('训练集数据：\n', X_train,'\n', y_train,'\n测试集数据\n', X_test,'\n', y_test)
tt = "single"
if tt == "all":
names = ["Nearest Neighbors", "Linear SVM", "RBF SVM", "GBDT",
"Decision Tree", "Random Forest", "Neural Net", "AdaBoost", "Naive Bayes"]
classifiers = [
KNeighborsClassifier(3),
SVC(kernel="linear", C=0.025),
SVC(gamma=2, C=1),
GradientBoostingClassifier(n_estimators=100, learning_rate=0.01, max_depth=1, random_state=0),
# GaussianProcessClassifier(1.0 * RBF(1.0)),
DecisionTreeClassifier(max_depth=5),
RandomForestClassifier(max_depth=10, n_estimators=10, max_features=1),
MLPClassifier(alpha=1, max_iter=500),
AdaBoostClassifier(),
GaussianNB()]
elif tt == "single":
names = ["Random Forest"]
classifiers = [RandomForestClassifier(max_depth=10, n_estimators=50, max_features=8)]
score = []
# iterate over classifiers
for name, clf in zip(names, classifiers):
clf.fit(X_train, y_train)
tmp = clf.score(X_test, y_test)
print(clf.predict(X_test), y_test)
# joblib.dump(clf, 'RF_10_50_8_%s.m'%tmp)
score.append(tmp)
print(name, tmp)
# xxx = np.logspace(-4, -1, 4)
# zzz = [50, 100, 200]
# yyy = [1, 2, 3, 4]
# print(xxx)
# # xxx=[1.2]
#
# a = 100
# a1 = 10000
#
# scores = []
# scores1 = []
# count = 0
# aaaa = np.ones((len(xxx),len(xxx),len(zzz)), dtype=np.float64)
# aaaa = aaaa*1000
#
# for zz, vz in zip(range(len(zzz)), zzz):
# for yy in yyy:
# for ii, vi in zip(range(len(xxx)), xxx):
# for jj, vj in zip(range(len(xxx)), xxx):
# print('循环次数', zz, yy, ii, jj)
# # clf = KNeighborsRegressor(int(ii), weights='uniform')
# clf1 = MLPRegressor(hidden_layer_sizes=(vz, yy), alpha=vi, solver='sgd', learning_rate_init=vj, max_iter=1000)
# clf1.fit(x_train, y_train)
# y_predict = clf1.predict(x_test)
# if np.isnan(y_predict).any() == True:
# continue
# MAE_TEMP1 = mean_absolute_error(y_predict, y_test)
# # aaaa[ii][jj][zz] = MAE_TEMP1
# print(MAE_TEMP1)
# if MAE_TEMP1 < a1:
# joblib.dump(clf1, 'clf_NN_3D_1214.m')
# a1 = MAE_TEMP1
#
# # MAE_TEMP = cross_val_score(clf, XXX, Y, cv=5, scoring='neg_mean_absolute_error')
# # MAE_TEMP1 = cross_val_score(clf1, x_train, y_train, cv=5, scoring='neg_mean_absolute_error')
# # # MAE_TEMP = -MAE_TEMP
# # MAE_TEMP1 = -MAE_TEMP1
# # print(ii, MAE_TEMP1, MAE_TEMP1)
# # # scores.append(MAE_TEMP.mean())
# # scores1.append(MAE_TEMP1.mean())
# # if MAE_TEMP1.mean() < a1:
# # clf1.fit(x_train, y_train)
# # joblib.dump(clf1, 'clf_NN_25D_CV5_all.m')
# # a1 = MAE_TEMP1.mean()
#
# count += 1
#
# # print(scores)
# print(aaaa)
# print('最低MAE',a, a1)
# # scores_np = np.array(scores, dtype=float)
# scores_np1 = np.array(scores1,dtype=float)
# # print(scores_np, scores_np1)
# # np.savetxt('uniform.txt',scores_np)
# # np.savetxt('distance.txt',aaaa)
#
#
# clf_best = joblib.load('clf_NN_3D_1214.m') #加载离线预测模型
# print(clf_best.get_params())
# y_predict1=clf_best.predict(XXX)
# print(y_predict1)
# print(Y)
# print('all MSE', mean_absolute_error(y_predict1, Y))
# import matplotlib.pyplot as plt
# from matplotlib import cm
# from mpl_toolkits.mplot3d import axes3d
#
# # x = range(1, len(scores_np1)+1)
# # # plt.plot(x, scores_np, marker='^', c='r', linewidth=1, label='Weights = "uniform"')
# # plt.plot(x, scores_np1, marker='o', c='b', linewidth=1, label='Weights = "distance"')
# # plt.legend()
# # plt.xlabel('n_neighbors')
# # plt.ylabel('MAE')
# # plt.grid(linestyle=':')
# # plt.show()
# X,Y=np.meshgrid(xxx,xxx)
# fig=plt.figure()
# ax1 = fig.add_subplot(141, projection='3d')
# ax1.gca(projection='3d')
# # ax.plot_surface(X,Y,zzz,cmap=cm.hot)
# ax1.plot_surface(X, Y, aaaa[:,:,0], cmap=cm.coolwarm,
# linewidth=0, antialiased=False)
# ax2 = fig.add_subplot(142)
# ax2.plot(xxx, )
#
# plt.show()
# from mpl_toolkits.mplot3d import Axes3D
# import matplotlib.pyplot as plt
# from matplotlib import cm
# from matplotlib.ticker import LinearLocator, FormatStrFormatter
# import numpy as np
#
# fig = plt.figure()
# ax = fig.gca(projection='3d')
#
# # Make data.
# X = np.arange(-5, 5, 0.25)
# Y = np.arange(-5, 5, 0.25)
# X, Y = np.meshgrid(X, Y)
# R = np.sqrt(X ** 2 + Y ** 2)
# Z = np.sin(R)
#
# # Plot the surface.
# surf = ax.plot_surface(X, Y, Z, cmap=cm.coolwarm,
# linewidth=0, antialiased=False)
#
# # Customize the z axis.
# ax.set_zlim(-1.01, 1.01)
# ax.zaxis.set_major_locator(LinearLocator(10))
# ax.zaxis.set_major_formatter(FormatStrFormatter('%.02f'))
#
# # Add a color bar which maps values to colors.
# fig.colorbar(surf, shrink=0.5, aspect=5)
#
# plt.show()