-
Notifications
You must be signed in to change notification settings - Fork 8
/
Copy pathcomparison.py
203 lines (165 loc) · 7.39 KB
/
comparison.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
198
199
200
201
# -*- coding:utf-8 -*-
import numpy as np
import pandas as pd
from sklearn import metrics, svm
from sklearn.neural_network import MLPClassifier
from sklearn.ensemble import RandomForestClassifier
from unsupervised_pretraining.dbn_.models import SupervisedDBNClassification
from sklearn.metrics import accuracy_score
from sklearn.metrics import cohen_kappa_score
from utils import cal_measure
import shap
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings("ignore")
"""model-agnostic SHAP"""
def SHAP_(predict_proba, x_train, x_test, f_name):
shap.initjs()
# SHAP demo are using dataframe instead of nparray
x_train = pd.DataFrame(x_train) # 将numpy的array数组x_test转为dataframe格式。
x_test = pd.DataFrame(x_test)
x_train.columns = f_name # 添加特征名称
x_test.columns = f_name
explainer = shap.KernelExplainer(predict_proba, shap.kmeans(x_train, 100))
x_ = x_test
shap_values = explainer.shap_values(x_, nsamples=100) # shap_values(_prob, n_samples, features)
# shap.force_plot(explainer.expected_value[1], shap_values[1][0, :], x_test.iloc[0, :], show=True, matplotlib=True) # single feature
shap.summary_plot(shap_values, x_, plot_type="bar", show=False)
plt.savefig('tmp/bar_HK.pdf')
plt.close()
shap.summary_plot(shap_values[1], x_, plot_type="violin", show=False) # shap_values[k], k表类别，k=1（landslides）
plt.savefig('tmp/violin_HK.pdf')
plt.close()
# shap.summary_plot(shap_values[1], x_test, plot_type="compact_dot")
# shap.plots.beeswarm(shap_values[0]) # the beeswarm plot requires Explanation object as the `shap_values` argument
def pred_LSM(trained_model, xy, samples, name):
"""LSM prediction"""
pred = trained_model.predict_proba(samples)
data = np.hstack((xy, pred))
data_df = pd.DataFrame(data)
writer = pd.ExcelWriter('./tmp/'+name+'_prediction_HK.xlsx')
data_df.to_excel(writer, 'page_1', float_format='%.5f')
writer.close()
def SVM_(x_train, y_train, x_test, y_test):
"""predict and test"""
print('start SVM evaluation...')
model = svm.SVC(C=1, kernel='rbf', gamma=1 / (2 * x_train.var()), decision_function_shape='ovr', probability=True)
# clf = svm.SVC(C=0.1, kernel='linear', decision_function_shape='ovr')
model.fit(x_train, y_train)
pred_train = model.predict(x_train)
print('train accuracy:' + str(metrics.accuracy_score(y_train, pred_train)))
pred_test = model.predict(x_test)
print('test accuracy:' + str(metrics.accuracy_score(y_test, pred_test)))
# Precision, Recall, F1-score
cal_measure(pred_test, y_test)
kappa_value = cohen_kappa_score(pred_test, y_test)
print('Cohen_Kappa: %f' % kappa_value)
# feature permutation
print('SHAP...')
SHAP_(model.predict_proba, x_train, x_test, f_names)
return model
# can be deprecated
def ANN_(x_train, y_train, x_test, y_test):
"""predict and test"""
print('start ANN evaluation...')
model = MLPClassifier(hidden_layer_sizes=(32, 32, 16), activation='relu', solver='adam', alpha=0.01,
batch_size=32, max_iter=1000)
model.fit(x_train, y_train)
pred_train = model.predict(x_train)
print('Train Accuracy: %f' % accuracy_score(y_train, pred_train))
pred_test = model.predict(x_test)
print('Test Accuracy: %f' % accuracy_score(y_test, pred_test))
# Precision, Recall, F1-score
cal_measure(pred_test, y_test)
kappa_value = cohen_kappa_score(pred_test, y_test)
print('Cohen_Kappa: %f' % kappa_value)
# SHAP
print('SHAP...')
# SHAP_(model.predict_proba, x_train, x_test, f_names)
return model
def DBN_(x_train, y_train, x_test, y_test):
print('start DBN evaluation...')
# Training
model = SupervisedDBNClassification(hidden_layers_structure=[32, 32],
learning_rate_rbm=0.001,
learning_rate=0.5,
n_epochs_rbm=10,
n_iter_backprop=200,
batch_size=64,
activation_function='relu',
dropout_p=0.1)
model.fit(x_train, y_train)
pred_train = np.array(model.predict(x_train))
pred_test = np.array(model.predict(x_test))
# 训练精度
print('train_Accuracy: %f' % accuracy_score(y_train, pred_train))
# 测试精度
print('test_Accuracy: %f' % accuracy_score(y_test, pred_test))
# pred1 = clf2.predict_proba() # 预测类别概率
cal_measure(pred_test, y_test)
kappa_value = cohen_kappa_score(pred_test, y_test)
print('Cohen_Kappa: %f' % kappa_value)
# SHAP
print('SHAP...')
# SHAP_(model.predict_proba, x_train, x_test, f_names)
return model
def RF_(x_train, y_train, x_test, y_test):
"""predict and test"""
print('start RF evaluation...')
model = RandomForestClassifier(n_estimators=200, max_depth=None)
model.fit(x_train, y_train)
pred_train = model.predict(x_train)
pred_test = model.predict(x_test)
# 训练精度
print('train_Accuracy: %f' % accuracy_score(y_train, pred_train))
# 测试精度
print('test_Accuracy: %f' % accuracy_score(y_test, pred_test))
# pred1 = clf2.predict_proba() # 预测类别概率
cal_measure(pred_test, y_test)
kappa_value = cohen_kappa_score(pred_test, y_test)
print('Cohen_Kappa: %f' % kappa_value)
# SHAP
print('SHAP...')
# TODO: SHAP for RF
# SHAP_(model.predict_proba, x_train, x_test, f_names)
shap.initjs()
explainer = shap.Explainer(model)
shap_values = explainer(x_train)
shap.plots.bar(shap_values[:100, :, 0]) # shap_values(n_samples, features, _prob)
return model
if __name__ == "__main__":
"""Input data"""
tmp = np.loadtxt('./src_data/samples_HK.csv', dtype=str, delimiter=",", encoding='UTF-8')
f_names = tmp[0, :-3].astype(np.str)
tmp_ = np.hstack((tmp[1:, :-3], tmp[1:, -1].reshape(-1, 1))).astype(np.float32)
np.random.shuffle(tmp_) # shuffle
# 训练集
x_train = tmp_[:int(tmp_.shape[0] / 4 * 3), :-1] # 加载i行数据部分
y_train = tmp_[:int(tmp_.shape[0] / 4 * 3), -1] # 加载类别标签部分
x_train = x_train / x_train.max(axis=0)
# 测试集
x_test = tmp_[int(tmp_.shape[0] / 4 * 3):, :-1] # 加载i行数据部分
y_test = tmp_[int(tmp_.shape[0] / 4 * 3):, -1] # 加载类别标签部分
x_test = x_test / x_test.max(axis=0)
# grid samples
grid_f = np.loadtxt('./src_data/grid_samples_HK.csv', dtype=str, delimiter=",", encoding='UTF-8')
samples_f = grid_f[1:, :-2].astype(np.float32)
xy = grid_f[1:, -2:].astype(np.float32)
samples_f = samples_f / samples_f.max(axis=0)
"""evaluate and save LSM result"""
# SVM-based
# model_svm = SVM_(x_train, y_train, x_test, y_test)
# pred_LSM(model_svm, xy, samples_f, 'SVM')
# print('done SVM-based LSM prediction! \n')
# # MLP_based
# model_mlp = ANN_(x_train, y_train, x_test, y_test)
# pred_LSM(model_mlp, xy, samples_f, 'MLP')
# print('done MLP-based LSM prediction! \n')
# # DBN-based
# model_dbn = DBN_(x_train, y_train, x_test, y_test)
# pred_LSM(model_dbn, xy, samples_f, 'DBN')
# print('done DBN-based LSM prediction! \n')
# RF-based
model_rf = RF_(x_train, y_train, x_test, y_test)
pred_LSM(model_rf, xy, samples_f, 'RF')
print('done RF-based LSM prediction! \n')