-
Notifications
You must be signed in to change notification settings - Fork 0
/
Higgins_Lauren_Code_Final.py
384 lines (278 loc) · 12.8 KB
/
Higgins_Lauren_Code_Final.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
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
'''
Loading the two data sets into my code and converting the dictionaries into numpy arrays.
'''
from scipy.io import loadmat
#Load datasets.
P = loadmat('/Users/laurenhiggins/Dropbox/Machine_Learning/Projects/BCdata/P.mat', mat_dtype=True)
T = loadmat('/Users/laurenhiggins/Dropbox/Machine_Learning/Projects/BCdata/T.mat', mat_dtype=True)
#Convert dictionaries into numpy arrays.
P_array = P['P'].T
T_array = T['T'].T
'''
Create a supervised classification dataset and arrange the target data between P and T.
For task 1 randomly 70% of the data with be used for 'training'
This step automatically labels the data as two different classes.
'''
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import label_binarize
# Binarize the output
T_binary = label_binarize(T_array, classes=[0, 1])
#Supervised classification dataset.
P_train, P_test, T_train, T_test = train_test_split(P_array, T_binary, test_size=0.3,
train_size=0.7, random_state=66)
'''
Standardize the input data.
'''
from sklearn.preprocessing import StandardScaler
# standardizing data for effecient use in SVM
scaler = StandardScaler()
scaler.fit(P_train)
P_train_st = scaler.transform(P_train)
P_test_st = scaler.transform(P_test)
'''
Setting up my loop that will go through the low bias, high variance classifiers. They are:
Fisher LDA --> LinearDiscriminantAnalysis()
Quadratic Discriminant Analysis --> QuadraticDiscriminantAnalysis()
Gaussian SVM: Radial Basis Function with low s^2 with hard margins --> svm.SVC(C=1, kernel='rbf', gamma='auto', probability=True)
3rd Order Polynomial SVM with hard margins --> vm.SVC(C=1, kernel='poly', degree=3, gamma='auto', probability=True)
Before the classification loop, I will utilize the sklearn.feature_selection package to utilize
forward/backward feature selection to create a subset of features for my four classifiers to
use in classification.
BONUS: Within the loop, I will also use stack generalization to create a meta learner.
'''
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis, QuadraticDiscriminantAnalysis
from sklearn import svm
classifiers = [
LinearDiscriminantAnalysis(),
QuadraticDiscriminantAnalysis(),
svm.SVC(C=1, kernel='rbf', gamma='auto', probability=True),
svm.SVC(C=1, kernel='poly', degree=3, gamma='auto', probability=True)]
class_type = ['FLDA', 'QuadraticDiscriminantAnalysis', 'RBF SVM', '3rd order SVM']
classifiers2 = [
LinearDiscriminantAnalysis(),
QuadraticDiscriminantAnalysis(),
svm.SVC(C=1, kernel='rbf', gamma='auto', probability=True),
svm.SVC(C=1, kernel='poly', degree=3, gamma='auto', probability=True)]
class_type2 = ['FLDA', 'QuadraticDiscriminantAnalysis', 'RBF SVM', '3rd order SVM']
classifiers3 = [
LinearDiscriminantAnalysis(),
QuadraticDiscriminantAnalysis(),
svm.SVC(C=1, kernel='rbf', gamma='auto', probability=True),
svm.SVC(C=1, kernel='poly', degree=3, gamma='auto', probability=True)]
# Dictionaries for non-preprocessed
model = {}
probs = {}
probst = {}
probs_pos = {}
probst_pos = {}
auc = {}
auct = {}
fpr= {}
tpr = {}
fprt = {}
tprt = {}
s = {}
st = {}
# Dictionaries for sfs preprocessed
model_sfs = {}
probs_sfs = {}
probst_sfs = {}
probs_sfs_pos = {}
probst_sfs_pos = {}
auc_sfs = {}
auct_sfs = {}
fpr_sfs = {}
tpr_sfs = {}
fprt_sfs = {}
tprt_sfs = {}
s_sfs = {}
st_sfs = {}
# Dictionaries for fusion classification
model_fus = {}
probs_fus = {}
probst_fus = {}
probs_fus_pos = {}
probst_fus_pos = {}
auc_fus = {}
auct_fus = {}
fpr_fus = {}
tpr_fus = {}
fprt_fus = {}
tprt_fus = {}
s_fus = {}
st_fus = {}
'''
Preprocessing using sequential forward feature selection. The python equivalent
to 'sequentialfs' is 'SequentialFeatureSelector'.
'''
from sklearn.feature_selection import SequentialFeatureSelector
from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier(n_neighbors=4)
sfs = SequentialFeatureSelector(knn, n_features_to_select=15, direction='backward')
P_train_sfs = sfs.fit_transform(P_train_st, T_train)
P_test_sfs = sfs.transform(P_test_st)
'''
Training the classifiers with original data and feature selected data.
'''
i = 0
for clf, clf_sfs, name, name2 in zip(classifiers, classifiers2, class_type, class_type2):
'''
Four low bias, high variance classifiers on original data.
'''
# training the models
model[name] = clf.fit(P_train_st, T_train)
probs[name] = model[name].predict_proba(P_test_st)
probst[name] = model[name].predict_proba(P_train_st)
# keep probabilities for the positive outcome only
probs_pos[name] = probs[name][:, 1]
probst_pos[name] = probst[name][:, 1]
'''
Four additional classifiers using feature selected data.
'''
model_sfs[name2] = clf_sfs.fit(P_train_sfs, T_train)
probs_sfs[name2] = model_sfs[name2].predict_proba(P_test_sfs)
probst_sfs[name2] = model_sfs[name2].predict_proba(P_train_sfs)
# keep probabilities for the positive outcome only
probs_sfs_pos[name2] = probs_sfs[name2][:, 1]
probst_sfs_pos[name2] = probst_sfs[name2][:, 1]
'''
**BONUS**
Stack generalization to train a meta learner of your choice
'''
from sklearn.ensemble import StackingClassifier
from sklearn.linear_model import LogisticRegression
estimators = [
('lda', LinearDiscriminantAnalysis()),
('qda', QuadraticDiscriminantAnalysis()),
('rbf', svm.SVC(C=1, kernel='rbf', gamma='auto', probability=True)),
('poly', svm.SVC(C=1, kernel='poly', degree=3, gamma='auto', probability=True))]
# training stack generalization
clf_stack = StackingClassifier(estimators=estimators, final_estimator=LogisticRegression())
model_stack = clf_stack.fit(P_train_st, T_train)
probs_stack = clf_stack.predict_proba(P_test_st)
probst_stack = clf_stack.predict_proba(P_train_st)
# keep probabilities for the positive outcome only
probs_stack_pos = probs_stack[:, 1]
probst_stack_pos = probst_stack[:, 1]
'''
AUC Scores for my four base classifiers both with original data and feature selected data and
my stack generalization scores.
'''
from sklearn.metrics import roc_curve
from sklearn.metrics import roc_auc_score
# score
auc[name] = roc_auc_score(T_test, probs_pos[name])
auct[name] = roc_auc_score(T_train, probst_pos[name])
auc_sfs[name2] = roc_auc_score(T_test, probs_sfs_pos[name])
auct_sfs[name2] = roc_auc_score(T_train, probst_sfs_pos[name])
auc_stack = roc_auc_score(T_test, probs_stack_pos)
auct_stack = roc_auc_score(T_train, probst_stack_pos)
# summarize scores for plots
s[name] = '' + class_type[i] + ': ROC Score=%.5f' % (auc[name])
st[name] = '' + class_type[i] + ': ROC Score=%.5f' % (auct[name])
s_sfs[name2] = '' + class_type2[i] + ' Backward FS: ROC Score=%.5f' % (auc_sfs[name])
st_sfs[name2] = '' + class_type2[i] + ' Backward FS: ROC Score=%.5f' % (auct_sfs[name])
st_stack = 'BONUS Stack Generalization: ROC Score=%.5f' % (auct_stack)
# calculate roc curves
fpr[name], tpr[name], _ = roc_curve(T_test, probs_pos[name])
fprt[name], tprt[name], _ = roc_curve(T_train, probst_pos[name])
fpr_sfs[name], tpr_sfs[name], _ = roc_curve(T_test, probs_sfs_pos[name])
fprt_sfs[name], tprt_sfs[name], _ = roc_curve(T_train, probst_sfs_pos[name])
fpr_stack, tpr_stack, _ = roc_curve(T_test, probs_stack_pos)
fprt_stack, tprt_stack, _ = roc_curve(T_train, probst_stack_pos)
'''
Fusion Ensembles:
Per table 18.2, I will choose max and sum for the best fusion classifier.
I will use the python modue VotingClassifier to use the sum fusion method.
'''
eclf = {}
for name3, name4 in zip(model, model_sfs):
# Create my sum fusion classifiers
from sklearn.ensemble import VotingClassifier
eclf1 = VotingClassifier(estimators=[('og_1', model['FLDA']),
('og2_1', model['QuadraticDiscriminantAnalysis']),
('pre_1', model_sfs['FLDA']),
('pre2_1', model_sfs['QuadraticDiscriminantAnalysis']),
('stack_1', clf_stack)],
voting='soft')
eclf2 = VotingClassifier(estimators=[('og_2', model['3rd order SVM']),
('og2_2', model['RBF SVM']),
('pre_2', model_sfs['3rd order SVM']),
('pre2_2', model_sfs['RBF SVM']),
('pre3_2', model_sfs['FLDA']),
('stack_2', clf_stack)],
voting='soft')
eclf3 = VotingClassifier(estimators=[('og_3', model['RBF SVM']),
('og2_3', model['3rd order SVM']),
('pre_3', model_sfs['RBF SVM']),
('pre2_3', model_sfs['FLDA']),
('stack_3', clf_stack)],
voting='soft')
eclf4 = VotingClassifier(estimators=[('og_4', model['RBF SVM']),
('og2_4', model['3rd order SVM']),
('pre_4', model_sfs['RBF SVM']),
('pre2_4', model_sfs['3rd order SVM']),
('stack_4', clf_stack)],
voting='soft')
eclf = {'FLDA': eclf1,
'QuadraticDiscriminantAnalysis': eclf2,
'RBF SVM': eclf3,
'3rd order SVM': eclf4
}
'''
Caclulating AUC scores for the sum fusion results.
'''
for vers in eclf:
model_fus[vers] = eclf[vers].fit(P_train_st, T_train)
probs_fus[vers] = model_fus[vers].predict_proba(P_test_st)
probst_fus[vers] = model_fus[vers].predict_proba(P_train_st)
probs_fus_pos[vers] = probs_fus[vers][:, 1]
probst_fus_pos[vers] = probst_fus[vers][:, 1]
# score
auc_fus[vers] = roc_auc_score(T_test, probs_fus_pos[vers])
auct_fus[vers] = roc_auc_score(T_train, probst_fus_pos[vers])
# summarize scores for plots
s_fus[vers] = 'Sum Fusion: ROC Score=%.5f' % (auc_fus[vers])
st_fus[vers] = 'Sum Fusion: ROC Score=%.5f' % (auct_fus[vers])
# calculate roc curves
fpr_fus[vers], tpr_fus[vers], _ = roc_curve(T_test, probs_fus_pos[vers])
fprt_fus[vers], tprt_fus[vers], _ = roc_curve(T_train, probst_fus_pos[vers])
'''
Plotting the top three fusion results and compairing them to the original ROC for all classifiers.
'''
i=0
eclf_type = ['eclf1', 'eclf2', 'eclf3', 'eclf4']
for key, key, key, key, key, key, key, key, key in zip(fpr.keys(), tpr.keys(), s.keys(), fpr_sfs.keys(), tpr_sfs.keys(),
s_sfs.keys(), fprt_fus.keys(), tpr_fus.keys(), s_fus.keys()):
# plot the roc curve for the models
import matplotlib.pyplot as plt
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(20,10))
ax1.plot(fpr[key], tpr[key], marker='.', label='Training '+ s[key], linestyle='-.')
ax1.plot(fpr_sfs[key], tpr_sfs[key], marker='.', label='Training '+s_sfs[key],
linestyle='-')
ax1.plot(fpr_fus['QuadraticDiscriminantAnalysis'],
tpr_fus['QuadraticDiscriminantAnalysis'], marker='o',
label='Training V4 '+s_fus['QuadraticDiscriminantAnalysis'],
linestyle='-')
ax2.plot(fprt[key], tprt[key], marker='.', label='Testing '+st[key], linestyle='-.')
ax2.plot(fprt_sfs[key], tprt_sfs[key], marker='.', label='Testing '+st_sfs[key],
linestyle='-')
ax2.plot(fprt_stack, tprt_stack, marker='*', label='Testing '+st_stack, linestyle=':')
ax2.plot(fprt_fus['QuadraticDiscriminantAnalysis'],
tprt_fus['QuadraticDiscriminantAnalysis'], marker='o',
label='Testing V4 '+st_fus['QuadraticDiscriminantAnalysis'],
linestyle='-')
# axis labels
ax1.set_xlabel('False Positive Rate', fontsize=14)
ax1.set_ylabel('True Positive Rate', fontsize=14)
ax2.set_xlabel('False Positive Rate', fontsize=14)
ax2.set_ylabel('True Positive Rate', fontsize=14)
ax1.set_title(class_type[i] + ' Training ROC', fontsize=18)
ax2.set_title(class_type[i] + ' Testing ROC', fontsize=18)
ax1.legend(loc='lower right', fontsize='x-large')
ax2.legend(loc='lower right', fontsize='x-large')
plt.savefig('Higgins_Lauren_ROC_Curves_eclf2_' + class_type[i] + '.pdf', bbox='tight')
plt.show()
plt.close(fig)
i += 1
# end