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asenella
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reproducing_mopoe_2

pythae
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Create metrics.log

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  1. metrics.log +198 -0
metrics.log ADDED
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+ Subset ('m0',) accuracies
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+ {'m1': 0.6553, 'm2': 0.6929, 'm3': 0.6706, 'm4': 0.3879}
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+ Mean subset ('m0',) accuracies : 0.601675
4
+ Subset ('m1',) accuracies
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+ {'m0': 0.6003, 'm2': 0.8225, 'm3': 0.8014, 'm4': 0.4185}
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+ Mean subset ('m1',) accuracies : 0.660675
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+ Subset ('m2',) accuracies
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+ {'m0': 0.5994, 'm1': 0.745, 'm3': 0.7634, 'm4': 0.4321}
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+ Mean subset ('m2',) accuracies : 0.6349750000000001
10
+ Subset ('m3',) accuracies
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+ {'m0': 0.5874, 'm1': 0.7106, 'm2': 0.7611, 'm4': 0.3805}
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+ Mean subset ('m3',) accuracies : 0.6099
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+ Subset ('m4',) accuracies
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+ {'m0': 0.5231, 'm1': 0.6164, 'm2': 0.6526, 'm3': 0.6425}
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+ Mean subset ('m4',) accuracies : 0.60865
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+ Subset ('m0', 'm1') accuracies
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+ {'m2': 0.9113, 'm3': 0.8916, 'm4': 0.4715}
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+ Mean subset ('m0', 'm1') accuracies : 0.7581333333333333
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+ Subset ('m0', 'm2') accuracies
20
+ {'m1': 0.8611, 'm3': 0.8683, 'm4': 0.4744}
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+ Mean subset ('m0', 'm2') accuracies : 0.7346
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+ Subset ('m0', 'm3') accuracies
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+ {'m1': 0.8411, 'm2': 0.882, 'm4': 0.4446}
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+ Mean subset ('m0', 'm3') accuracies : 0.7225666666666667
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+ Subset ('m0', 'm4') accuracies
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+ {'m1': 0.8076, 'm2': 0.8419, 'm3': 0.8209}
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+ Mean subset ('m0', 'm4') accuracies : 0.8234666666666666
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+ Subset ('m1', 'm2') accuracies
29
+ {'m0': 0.6707, 'm3': 0.9068, 'm4': 0.4862}
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+ Mean subset ('m1', 'm2') accuracies : 0.6879000000000001
31
+ Subset ('m1', 'm3') accuracies
32
+ {'m0': 0.6759, 'm2': 0.9135, 'm4': 0.4582}
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+ Mean subset ('m1', 'm3') accuracies : 0.6825333333333333
34
+ Subset ('m1', 'm4') accuracies
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+ {'m0': 0.6602, 'm2': 0.9042, 'm3': 0.882}
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+ Mean subset ('m1', 'm4') accuracies : 0.8154666666666667
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+ Subset ('m2', 'm3') accuracies
38
+ {'m0': 0.6568, 'm1': 0.8582, 'm4': 0.458}
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+ Mean subset ('m2', 'm3') accuracies : 0.6576666666666667
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+ Subset ('m2', 'm4') accuracies
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+ {'m0': 0.6542, 'm1': 0.8429, 'm3': 0.8494}
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+ Mean subset ('m2', 'm4') accuracies : 0.7821666666666668
43
+ Subset ('m3', 'm4') accuracies
44
+ {'m0': 0.6504, 'm1': 0.8341, 'm2': 0.8772}
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+ Mean subset ('m3', 'm4') accuracies : 0.7872333333333333
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+ Subset ('m0', 'm1', 'm2') accuracies
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+ {'m3': 0.9355, 'm4': 0.4992}
48
+ Mean subset ('m0', 'm1', 'm2') accuracies : 0.7173499999999999
49
+ Subset ('m0', 'm1', 'm3') accuracies
50
+ {'m2': 0.9518, 'm4': 0.4745}
51
+ Mean subset ('m0', 'm1', 'm3') accuracies : 0.71315
52
+ Subset ('m0', 'm1', 'm4') accuracies
53
+ {'m2': 0.946, 'm3': 0.9251}
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+ Mean subset ('m0', 'm1', 'm4') accuracies : 0.93555
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+ Subset ('m0', 'm2', 'm3') accuracies
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+ {'m1': 0.9094, 'm4': 0.4773}
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+ Mean subset ('m0', 'm2', 'm3') accuracies : 0.69335
58
+ Subset ('m0', 'm2', 'm4') accuracies
59
+ {'m1': 0.9067, 'm3': 0.9134}
60
+ Mean subset ('m0', 'm2', 'm4') accuracies : 0.91005
61
+ Subset ('m0', 'm3', 'm4') accuracies
62
+ {'m1': 0.8927, 'm2': 0.9269}
63
+ Mean subset ('m0', 'm3', 'm4') accuracies : 0.9097999999999999
64
+ Subset ('m1', 'm2', 'm3') accuracies
65
+ {'m0': 0.6859, 'm4': 0.4952}
66
+ Mean subset ('m1', 'm2', 'm3') accuracies : 0.5905499999999999
67
+ Subset ('m1', 'm2', 'm4') accuracies
68
+ {'m0': 0.678, 'm3': 0.9321}
69
+ Mean subset ('m1', 'm2', 'm4') accuracies : 0.80505
70
+ Subset ('m1', 'm3', 'm4') accuracies
71
+ {'m0': 0.6717, 'm2': 0.9474}
72
+ Mean subset ('m1', 'm3', 'm4') accuracies : 0.80955
73
+ Subset ('m2', 'm3', 'm4') accuracies
74
+ {'m0': 0.6716, 'm1': 0.9043}
75
+ Mean subset ('m2', 'm3', 'm4') accuracies : 0.7879499999999999
76
+ Subset ('m0', 'm1', 'm2', 'm3') accuracies
77
+ {'m4': 0.5001}
78
+ Mean subset ('m0', 'm1', 'm2', 'm3') accuracies : 0.5001
79
+ Subset ('m0', 'm1', 'm2', 'm4') accuracies
80
+ {'m3': 0.9502}
81
+ Mean subset ('m0', 'm1', 'm2', 'm4') accuracies : 0.9502
82
+ Subset ('m0', 'm1', 'm3', 'm4') accuracies
83
+ {'m2': 0.9686}
84
+ Mean subset ('m0', 'm1', 'm3', 'm4') accuracies : 0.9686
85
+ Subset ('m0', 'm2', 'm3', 'm4') accuracies
86
+ {'m1': 0.932}
87
+ Mean subset ('m0', 'm2', 'm3', 'm4') accuracies : 0.932
88
+ Subset ('m1', 'm2', 'm3', 'm4') accuracies
89
+ {'m0': 0.6784}
90
+ Mean subset ('m1', 'm2', 'm3', 'm4') accuracies : 0.6784
91
+ Conditional accuracies for 0 modalities : 0.623175 +- 0.021885800190991435
92
+ Conditional accuracies for 1 modalities : 0.7451733333333334 +- 0.05464502579782026
93
+ Conditional accuracies for 2 modalities : 0.7872349999999999 +- 0.10525719464720691
94
+ Conditional accuracies for 3 modalities : 0.80586 +- 0.18599186648883334
95
+ Joint coherence : 0.11299999803304672
96
+ Joint likelihood : tensor(9869.6572)
97
+ Joint likelihood : tensor(9869.6572)
98
+ Joint likelihood from subset ['m0', 'm1', 'm2', 'm3', 'm4'] : tensor(9359.4346)
99
+ Joint likelihood from subset ['m0', 'm1', 'm2', 'm3', 'm4'] : tensor(9359.4346)
100
+ Subset ('m0',) accuracies
101
+ {'m1': 0.6459, 'm2': 0.6941, 'm3': 0.6773, 'm4': 0.3899}
102
+ Mean subset ('m0',) accuracies : 0.6018
103
+ Subset ('m1',) accuracies
104
+ {'m0': 0.6023, 'm2': 0.8213, 'm3': 0.8028, 'm4': 0.4117}
105
+ Mean subset ('m1',) accuracies : 0.659525
106
+ Subset ('m2',) accuracies
107
+ {'m0': 0.6104, 'm1': 0.7504, 'm3': 0.7631, 'm4': 0.4405}
108
+ Mean subset ('m2',) accuracies : 0.6411
109
+ Subset ('m3',) accuracies
110
+ {'m0': 0.5829, 'm1': 0.7066, 'm2': 0.7573, 'm4': 0.3816}
111
+ Mean subset ('m3',) accuracies : 0.6071
112
+ Subset ('m4',) accuracies
113
+ {'m0': 0.525, 'm1': 0.6207, 'm2': 0.6525, 'm3': 0.6384}
114
+ Mean subset ('m4',) accuracies : 0.60915
115
+ Subset ('m0', 'm1') accuracies
116
+ {'m2': 0.9177, 'm3': 0.8918, 'm4': 0.4677}
117
+ Mean subset ('m0', 'm1') accuracies : 0.7590666666666666
118
+ Subset ('m0', 'm2') accuracies
119
+ {'m1': 0.8555, 'm3': 0.8679, 'm4': 0.4769}
120
+ Mean subset ('m0', 'm2') accuracies : 0.7334333333333333
121
+ Subset ('m0', 'm3') accuracies
122
+ {'m1': 0.8404, 'm2': 0.8825, 'm4': 0.4405}
123
+ Mean subset ('m0', 'm3') accuracies : 0.7211333333333334
124
+ Subset ('m0', 'm4') accuracies
125
+ {'m1': 0.8096, 'm2': 0.8436, 'm3': 0.8244}
126
+ Mean subset ('m0', 'm4') accuracies : 0.8258666666666666
127
+ Subset ('m1', 'm2') accuracies
128
+ {'m0': 0.6754, 'm3': 0.9021, 'm4': 0.4867}
129
+ Mean subset ('m1', 'm2') accuracies : 0.6880666666666667
130
+ Subset ('m1', 'm3') accuracies
131
+ {'m0': 0.6831, 'm2': 0.9153, 'm4': 0.4544}
132
+ Mean subset ('m1', 'm3') accuracies : 0.6842666666666667
133
+ Subset ('m1', 'm4') accuracies
134
+ {'m0': 0.6599, 'm2': 0.9068, 'm3': 0.8763}
135
+ Mean subset ('m1', 'm4') accuracies : 0.8143333333333334
136
+ Subset ('m2', 'm3') accuracies
137
+ {'m0': 0.6612, 'm1': 0.8607, 'm4': 0.4617}
138
+ Mean subset ('m2', 'm3') accuracies : 0.6612
139
+ Subset ('m2', 'm4') accuracies
140
+ {'m0': 0.6416, 'm1': 0.8487, 'm3': 0.8546}
141
+ Mean subset ('m2', 'm4') accuracies : 0.7816333333333333
142
+ Subset ('m3', 'm4') accuracies
143
+ {'m0': 0.6426, 'm1': 0.8337, 'm2': 0.8705}
144
+ Mean subset ('m3', 'm4') accuracies : 0.7822666666666667
145
+ Subset ('m0', 'm1', 'm2') accuracies
146
+ {'m3': 0.9379, 'm4': 0.5052}
147
+ Mean subset ('m0', 'm1', 'm2') accuracies : 0.7215499999999999
148
+ Subset ('m0', 'm1', 'm3') accuracies
149
+ {'m2': 0.9503, 'm4': 0.4788}
150
+ Mean subset ('m0', 'm1', 'm3') accuracies : 0.71455
151
+ Subset ('m0', 'm1', 'm4') accuracies
152
+ {'m2': 0.9495, 'm3': 0.928}
153
+ Mean subset ('m0', 'm1', 'm4') accuracies : 0.93875
154
+ Subset ('m0', 'm2', 'm3') accuracies
155
+ {'m1': 0.9089, 'm4': 0.4778}
156
+ Mean subset ('m0', 'm2', 'm3') accuracies : 0.69335
157
+ Subset ('m0', 'm2', 'm4') accuracies
158
+ {'m1': 0.9085, 'm3': 0.9114}
159
+ Mean subset ('m0', 'm2', 'm4') accuracies : 0.90995
160
+ Subset ('m0', 'm3', 'm4') accuracies
161
+ {'m1': 0.8907, 'm2': 0.931}
162
+ Mean subset ('m0', 'm3', 'm4') accuracies : 0.91085
163
+ Subset ('m1', 'm2', 'm3') accuracies
164
+ {'m0': 0.6839, 'm4': 0.4954}
165
+ Mean subset ('m1', 'm2', 'm3') accuracies : 0.58965
166
+ Subset ('m1', 'm2', 'm4') accuracies
167
+ {'m0': 0.6741, 'm3': 0.9315}
168
+ Mean subset ('m1', 'm2', 'm4') accuracies : 0.8028
169
+ Subset ('m1', 'm3', 'm4') accuracies
170
+ {'m0': 0.6853, 'm2': 0.9491}
171
+ Mean subset ('m1', 'm3', 'm4') accuracies : 0.8172
172
+ Subset ('m2', 'm3', 'm4') accuracies
173
+ {'m0': 0.6704, 'm1': 0.9033}
174
+ Mean subset ('m2', 'm3', 'm4') accuracies : 0.78685
175
+ Subset ('m0', 'm1', 'm2', 'm3') accuracies
176
+ {'m4': 0.4945}
177
+ Mean subset ('m0', 'm1', 'm2', 'm3') accuracies : 0.4945
178
+ Subset ('m0', 'm1', 'm2', 'm4') accuracies
179
+ {'m3': 0.9466}
180
+ Mean subset ('m0', 'm1', 'm2', 'm4') accuracies : 0.9466
181
+ Subset ('m0', 'm1', 'm3', 'm4') accuracies
182
+ {'m2': 0.9702}
183
+ Mean subset ('m0', 'm1', 'm3', 'm4') accuracies : 0.9702
184
+ Subset ('m0', 'm2', 'm3', 'm4') accuracies
185
+ {'m1': 0.9339}
186
+ Mean subset ('m0', 'm2', 'm3', 'm4') accuracies : 0.9339
187
+ Subset ('m1', 'm2', 'm3', 'm4') accuracies
188
+ {'m0': 0.6741}
189
+ Mean subset ('m1', 'm2', 'm3', 'm4') accuracies : 0.6741
190
+ Conditional accuracies for 0 modalities : 0.623735 +- 0.022596712150222225
191
+ Conditional accuracies for 1 modalities : 0.7451266666666667 +- 0.053787389682795415
192
+ Conditional accuracies for 2 modalities : 0.7885500000000001 +- 0.10576923702097885
193
+ Conditional accuracies for 3 modalities : 0.8038599999999999 +- 0.18841198051079447
194
+ Joint coherence : 0.11150000244379044
195
+ Joint likelihood : tensor(9869.5264)
196
+ Joint likelihood : tensor(9869.5264)
197
+ Joint likelihood from subset ['m0', 'm1', 'm2', 'm3', 'm4'] : tensor(9359.5586)
198
+ Joint likelihood from subset ['m0', 'm1', 'm2', 'm3', 'm4'] : tensor(9359.5586)