-
Notifications
You must be signed in to change notification settings - Fork 0
/
census.log
184 lines (184 loc) · 15.8 KB
/
census.log
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
2024-03-24 20:30:18.069 INFO [ START: Training... ]
2024-03-24 20:30:26.000 INFO [ FINISH: Training successful ]
2024-03-24 20:30:26.026 INFO [ METRICS: Precision: 0.7516891891891891, Recall: 0.5676020408163265, Fbeta: 0.6468023255813954 ]
2024-03-24 20:30:27.949 INFO [ FILE: Metrics written to ./artifacts/slice_output.txt ]
2024-03-24 20:30:27.952 INFO [ FILE: Model saved to file ./artifacts/model.joblib ]
2024-03-24 20:30:27.954 INFO [ FILE: Encoder saved to ./artifacts/encoder.joblib ]
2024-03-24 20:30:27.955 INFO [ FILE: Label binarizer saved to ./artifacts/lb.joblib ]
2024-03-24 20:30:47.389 INFO [ FastAPI ]
2024-03-24 20:30:47.390 INFO [ START: Loading model... ]
2024-03-24 20:30:47.398 INFO [ SUCCESS: Model loaded ]
2024-03-24 20:31:08.736 INFO [ CONTROL: Data dictonary: dict_values(['Private', 'Masters', 'Never-married', 'Prof-specialty', 'Not-in-family', 'White', 'Female', 'United-States', 31, 45781, 14, 14084, 0, 50]) ]
2024-03-24 20:31:08.740 INFO [ START: Model inference started ]
2024-03-24 20:31:08.772 INFO [ CONTROL: Input data: workclass education marital_status occupation relationship race sex native_country age fnlgt education_num capital_gain capital_loss hours_per_week
0 Private Masters Never-married Prof-specialty Not-in-family White Female United-States 31 45781 14 14084 0 50 ]
2024-03-24 20:31:08.776 INFO [ START: Cleaning data ]
2024-03-24 20:31:08.790 INFO [ CONTROL: Categorical features: ['workclass', 'education', 'marital_status', 'occupation', 'relationship', 'race', 'sex', 'native_country']
2024-03-24 20:31:08.791 INFO [ CONTROL: Numerical features: ['age', 'fnlgt', 'education_num', 'capital_gain', 'capital_loss', 'hours_per_week']
2024-03-24 20:31:08.792 INFO [ Cleaned data returned ]
2024-03-24 20:31:08.803 INFO [ MODEL: Run inference with input: [[3.1000e+01 4.5781e+04 1.4000e+01 1.4084e+04 0.0000e+00 5.0000e+01
0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 1.0000e+00 0.0000e+00
0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
0.0000e+00 0.0000e+00 0.0000e+00 1.0000e+00 0.0000e+00 0.0000e+00
0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 1.0000e+00
0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
1.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
1.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
0.0000e+00 0.0000e+00 0.0000e+00 1.0000e+00 1.0000e+00 0.0000e+00
0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
0.0000e+00 0.0000e+00 0.0000e+00 1.0000e+00 0.0000e+00 0.0000e+00]] ]
2024-03-24 20:31:08.841 INFO [ FINISH: Prediction completed ]
2024-03-24 20:31:08.841 INFO [ RESULT: The predicted income is: >50K ]
2024-03-26 19:32:28.657 INFO [ FastAPI ]
2024-03-26 19:32:28.658 INFO [ START: Loading model... ]
2024-03-26 19:32:28.663 INFO [ SUCCESS: Model loaded ]
2024-03-26 19:33:59.086 INFO [ CONTROL: Data dictonary: dict_values(['Private', 'Masters', 'Never-married', 'Prof-specialty', 'Not-in-family', 'White', 'Female', 'United-States', 31, 45781, 14, 14084, 0, 50]) ]
2024-03-26 19:33:59.087 INFO [ START: Model inference started ]
2024-03-26 19:33:59.116 INFO [ CONTROL: Input data: workclass education marital_status occupation relationship race sex native_country age fnlgt education_num capital_gain capital_loss hours_per_week
0 Private Masters Never-married Prof-specialty Not-in-family White Female United-States 31 45781 14 14084 0 50 ]
2024-03-26 19:33:59.116 INFO [ START: Cleaning data ]
2024-03-26 19:33:59.123 INFO [ CONTROL: Categorical features: ['workclass', 'education', 'marital_status', 'occupation', 'relationship', 'race', 'sex', 'native_country']
2024-03-26 19:33:59.123 INFO [ CONTROL: Numerical features: ['age', 'fnlgt', 'education_num', 'capital_gain', 'capital_loss', 'hours_per_week']
2024-03-26 19:33:59.124 INFO [ Cleaned data returned ]
2024-03-26 19:33:59.132 INFO [ MODEL: Run inference with input: [[3.1000e+01 4.5781e+04 1.4000e+01 1.4084e+04 0.0000e+00 5.0000e+01
0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 1.0000e+00 0.0000e+00
0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
0.0000e+00 0.0000e+00 0.0000e+00 1.0000e+00 0.0000e+00 0.0000e+00
0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 1.0000e+00
0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
1.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
1.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
0.0000e+00 0.0000e+00 0.0000e+00 1.0000e+00 1.0000e+00 0.0000e+00
0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
0.0000e+00 0.0000e+00 0.0000e+00 1.0000e+00 0.0000e+00 0.0000e+00]] ]
2024-03-26 19:33:59.180 INFO [ FINISH: Prediction completed ]
2024-03-26 19:33:59.181 INFO [ RESULT: The predicted income is: >50K ]
2024-03-26 20:20:29.506 INFO [ FastAPI ]
2024-03-26 20:20:29.507 INFO [ START: Loading model... ]
2024-03-26 20:20:29.514 INFO [ SUCCESS: Model loaded ]
2024-03-26 20:26:02.182 INFO [ CONTROL: Data dictonary: dict_values(['Private', 'Masters', 'Never-married', 'Prof-specialty', 'Not-in-family', 'White', 'Female', 'United-States', 31, 45781, 14, 14084, 0, 50]) ]
2024-03-26 20:26:02.185 INFO [ START: Model inference started ]
2024-03-26 20:26:02.237 INFO [ CONTROL: Input data: workclass education marital_status occupation relationship race sex native_country age fnlgt education_num capital_gain capital_loss hours_per_week
0 Private Masters Never-married Prof-specialty Not-in-family White Female United-States 31 45781 14 14084 0 50 ]
2024-03-26 20:26:02.238 INFO [ START: Cleaning data ]
2024-03-26 20:26:02.265 INFO [ CONTROL: Categorical features: ['workclass', 'education', 'marital_status', 'occupation', 'relationship', 'race', 'sex', 'native_country']
2024-03-26 20:26:02.266 INFO [ CONTROL: Numerical features: ['age', 'fnlgt', 'education_num', 'capital_gain', 'capital_loss', 'hours_per_week']
2024-03-26 20:26:02.267 INFO [ Cleaned data returned ]
2024-03-26 20:26:02.290 INFO [ MODEL: Run inference with input: [[3.1000e+01 4.5781e+04 1.4000e+01 1.4084e+04 0.0000e+00 5.0000e+01
0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 1.0000e+00 0.0000e+00
0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
0.0000e+00 0.0000e+00 0.0000e+00 1.0000e+00 0.0000e+00 0.0000e+00
0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 1.0000e+00
0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
1.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
1.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
0.0000e+00 0.0000e+00 0.0000e+00 1.0000e+00 1.0000e+00 0.0000e+00
0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
0.0000e+00 0.0000e+00 0.0000e+00 1.0000e+00 0.0000e+00 0.0000e+00]] ]
2024-03-26 20:26:02.355 INFO [ FINISH: Prediction completed ]
2024-03-26 20:26:02.357 INFO [ RESULT: The predicted income is: >50K ]
2024-03-26 20:26:33.096 INFO [ CONTROL: Data dictonary: dict_values(['Private', 'Masters', 'Never-married', 'Prof-specialty', 'Not-in-family', 'White', 'Female', 'United-States', 31, 45781, 14, 14084, 0, 50]) ]
2024-03-26 20:26:33.097 INFO [ START: Model inference started ]
2024-03-26 20:26:33.134 INFO [ CONTROL: Input data: workclass education marital_status occupation relationship race sex native_country age fnlgt education_num capital_gain capital_loss hours_per_week
0 Private Masters Never-married Prof-specialty Not-in-family White Female United-States 31 45781 14 14084 0 50 ]
2024-03-26 20:26:33.149 INFO [ START: Cleaning data ]
2024-03-26 20:26:33.158 INFO [ CONTROL: Categorical features: ['workclass', 'education', 'marital_status', 'occupation', 'relationship', 'race', 'sex', 'native_country']
2024-03-26 20:26:33.158 INFO [ CONTROL: Numerical features: ['age', 'fnlgt', 'education_num', 'capital_gain', 'capital_loss', 'hours_per_week']
2024-03-26 20:26:33.159 INFO [ Cleaned data returned ]
2024-03-26 20:26:33.166 INFO [ MODEL: Run inference with input: [[3.1000e+01 4.5781e+04 1.4000e+01 1.4084e+04 0.0000e+00 5.0000e+01
0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 1.0000e+00 0.0000e+00
0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
0.0000e+00 0.0000e+00 0.0000e+00 1.0000e+00 0.0000e+00 0.0000e+00
0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 1.0000e+00
0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
1.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
1.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
0.0000e+00 0.0000e+00 0.0000e+00 1.0000e+00 1.0000e+00 0.0000e+00
0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
0.0000e+00 0.0000e+00 0.0000e+00 1.0000e+00 0.0000e+00 0.0000e+00]] ]
2024-03-26 20:26:33.218 INFO [ FINISH: Prediction completed ]
2024-03-26 20:26:33.219 INFO [ RESULT: The predicted income is: >50K ]
2024-03-26 20:27:44.099 INFO [ CONTROL: Data dictonary: dict_values(['Private', '11th', 'Married-civ-spouse', 'Craft-repair', 'Husband', 'White', 'Male', 'United-States', 36, 186035, 7, 5178, 0, 40]) ]
2024-03-26 20:27:44.100 INFO [ START: Model inference started ]
2024-03-26 20:27:44.135 INFO [ CONTROL: Input data: workclass education marital_status occupation relationship race sex native_country age fnlgt education_num capital_gain capital_loss hours_per_week
0 Private 11th Married-civ-spouse Craft-repair Husband White Male United-States 36 186035 7 5178 0 40 ]
2024-03-26 20:27:44.144 INFO [ START: Cleaning data ]
2024-03-26 20:27:44.159 INFO [ CONTROL: Categorical features: ['workclass', 'education', 'marital_status', 'occupation', 'relationship', 'race', 'sex', 'native_country']
2024-03-26 20:27:44.159 INFO [ CONTROL: Numerical features: ['age', 'fnlgt', 'education_num', 'capital_gain', 'capital_loss', 'hours_per_week']
2024-03-26 20:27:44.160 INFO [ Cleaned data returned ]
2024-03-26 20:27:44.165 INFO [ MODEL: Run inference with input: [[3.60000e+01 1.86035e+05 7.00000e+00 5.17800e+03 0.00000e+00 4.00000e+01
0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.00000e+00 0.00000e+00
0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.00000e+00 0.00000e+00
0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00
0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00
0.00000e+00 0.00000e+00 0.00000e+00 1.00000e+00 0.00000e+00 0.00000e+00
0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.00000e+00
0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00
0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.00000e+00
0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00
0.00000e+00 0.00000e+00 0.00000e+00 1.00000e+00 0.00000e+00 1.00000e+00
0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00
0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00
0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00
0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00
0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00
0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00
0.00000e+00 0.00000e+00 0.00000e+00 1.00000e+00 0.00000e+00 0.00000e+00]] ]
2024-03-26 20:27:44.229 INFO [ FINISH: Prediction completed ]
2024-03-26 20:27:44.236 INFO [ RESULT: The predicted income is: >50K ]
2024-03-26 20:27:58.672 INFO [ CONTROL: Data dictonary: dict_values(['Private', 'Masters', 'Never-married', 'Prof-specialty', 'Not-in-family', 'White', 'Female', 'United-States', 31, 45781, 14, 14084, 0, 50]) ]
2024-03-26 20:27:58.673 INFO [ START: Model inference started ]
2024-03-26 20:27:58.699 INFO [ CONTROL: Input data: workclass education marital_status occupation relationship race sex native_country age fnlgt education_num capital_gain capital_loss hours_per_week
0 Private Masters Never-married Prof-specialty Not-in-family White Female United-States 31 45781 14 14084 0 50 ]
2024-03-26 20:27:58.710 INFO [ START: Cleaning data ]
2024-03-26 20:27:58.729 INFO [ CONTROL: Categorical features: ['workclass', 'education', 'marital_status', 'occupation', 'relationship', 'race', 'sex', 'native_country']
2024-03-26 20:27:58.730 INFO [ CONTROL: Numerical features: ['age', 'fnlgt', 'education_num', 'capital_gain', 'capital_loss', 'hours_per_week']
2024-03-26 20:27:58.731 INFO [ Cleaned data returned ]
2024-03-26 20:27:58.740 INFO [ MODEL: Run inference with input: [[3.1000e+01 4.5781e+04 1.4000e+01 1.4084e+04 0.0000e+00 5.0000e+01
0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 1.0000e+00 0.0000e+00
0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
0.0000e+00 0.0000e+00 0.0000e+00 1.0000e+00 0.0000e+00 0.0000e+00
0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 1.0000e+00
0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
1.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
1.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
0.0000e+00 0.0000e+00 0.0000e+00 1.0000e+00 1.0000e+00 0.0000e+00
0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
0.0000e+00 0.0000e+00 0.0000e+00 1.0000e+00 0.0000e+00 0.0000e+00]] ]
2024-03-26 20:27:58.825 INFO [ FINISH: Prediction completed ]
2024-03-26 20:27:58.827 INFO [ RESULT: The predicted income is: >50K ]