Changeset 9307:881b7133d305 in orange


Ignore:
Timestamp:
12/05/11 22:33:29 (2 years ago)
Author:
markotoplak
Branch:
default
Convert:
695d6036c18b1208b849a15512184b940c7022cf
Message:

Rewrote test results after relief fix @8997. Fixes #1025.

Location:
testing/regressionTests/results/orange
Files:
8 edited

Legend:

Unmodified
Added
Removed
  • testing/regressionTests/results/orange/modules/fss1.py.txt

    r9300 r9307  
    11Attribute scores for best three attributes: 
    2 0.752 physician-fee-freeze 
    3 0.365 el-salvador-aid 
    4 0.340 crime 
     20.613 physician-fee-freeze 
     30.255 el-salvador-aid 
     40.228 synfuels-corporation-cutback 
    55 
    66Best 3 attributes: 
    77physician-fee-freeze 
    88el-salvador-aid 
    9 crime 
     9synfuels-corporation-cutback 
  • testing/regressionTests/results/orange/modules/fss2.py.txt

    r9300 r9307  
    11Relief GainRt Attribute 
    2 0.752  0.752  physician-fee-freeze 
    3 0.365  0.444  el-salvador-aid 
    4 0.340  0.414  crime 
    5 0.297  0.382  synfuels-corporation-cutback 
    6 0.244  0.345  adoption-of-the-budget-resolution 
     20.613  0.752  physician-fee-freeze 
     30.255  0.444  el-salvador-aid 
     40.228  0.414  synfuels-corporation-cutback 
     50.189  0.382  crime 
     60.166  0.345  adoption-of-the-budget-resolution 
  • testing/regressionTests/results/orange/modules/fss3.py.txt

    r5824 r9307  
     1/home/marko/orangegit/trunk/testing/regressionTests/xtest1.py:11: DeprecationWarning: object.__new__() takes no parameters 
     2  def t__samefiles(name1, name2): 
    13Learner      CA 
    2 Naive Bayes  0.901 
    3 with FSS     0.940 
     4Naive Bayes  0.903 
     5with FSS     0.938 
  • testing/regressionTests/results/orange/modules/fss5.py.txt

    r9300 r9307  
    11Before feature subset selection: 
    2 0.280 marital-status 
    3 0.243 relationship 
    4 0.110 education 
    5 0.056 occupation 
    6 0.052 education-num 
    7 0.050 workclass 
    8 0.029 sex 
    9 0.024 age 
     20.098 workclass 
     30.056 marital-status 
     40.049 relationship 
    1050.022 race 
    11 0.018 hours-per-week 
    12 0.016 capital-gain 
    13 0.011 native-country 
    14 0.010 capital-loss 
    15 -0.003 fnlwgt 
     60.019 capital-gain 
     70.012 education-num 
     80.002 capital-loss 
     90.001 hours-per-week 
     10-0.002 age 
     11-0.006 native-country 
     12-0.011 occupation 
     13-0.014 education 
     14-0.022 fnlwgt 
     15-0.025 sex 
    1616 
    1717After feature subset selection with margin 0.010: 
    18 0.275 marital-status 
    19 0.222 relationship 
    20 0.123 education 
    21 0.059 education-num 
    22 0.059 workclass 
    23 0.054 occupation 
    24 0.040 sex 
    25 0.022 age 
    26 0.019 hours-per-week 
    27 0.016 capital-gain 
    28 0.015 race 
    29 0.010 capital-loss 
     180.107 workclass 
     190.093 relationship 
     200.065 marital-status 
     210.021 capital-gain 
     220.020 race 
     230.018 education-num 
  • testing/regressionTests/results/orange/modules/fss6.py.txt

    r9300 r9307  
    11Before feature subset selection (14 attributes): 
    2 0.280 marital-status 
    3 0.243 relationship 
    4 0.110 education 
    5 0.056 occupation 
    6 0.052 education-num 
    7 0.050 workclass 
    8 0.029 sex 
    9 0.024 age 
     20.098 workclass 
     30.056 marital-status 
     40.049 relationship 
    1050.022 race 
    11 0.018 hours-per-week 
    12 0.016 capital-gain 
    13 0.011 native-country 
    14 0.010 capital-loss 
    15 -0.003 fnlwgt 
     60.019 capital-gain 
     70.012 education-num 
     80.002 capital-loss 
     90.001 hours-per-week 
     10-0.002 age 
     11-0.006 native-country 
     12-0.011 occupation 
     13-0.014 education 
     14-0.022 fnlwgt 
     15-0.025 sex 
    1616 
    17 After feature subset selection with margin 0.010 (12 attributes): 
    18 0.275 marital-status 
    19 0.222 relationship 
    20 0.123 education 
    21 0.059 education-num 
    22 0.059 workclass 
    23 0.054 occupation 
    24 0.040 sex 
    25 0.022 age 
    26 0.019 hours-per-week 
    27 0.016 capital-gain 
    28 0.015 race 
    29 0.010 capital-loss 
     17After feature subset selection with margin 0.010 (6 attributes): 
     180.107 workclass 
     190.093 relationship 
     200.065 marital-status 
     210.021 capital-gain 
     220.020 race 
     230.018 education-num 
  • testing/regressionTests/results/orange/ofb/fss6.py.txt

    r9300 r9307  
    11Before feature subset selection (14 attributes): 
    2 0.280 marital-status 
    3 0.243 relationship 
    4 0.110 education 
    5 0.056 occupation 
    6 0.052 education-num 
    7 0.050 workclass 
    8 0.029 sex 
    9 0.024 age 
     20.098 workclass 
     30.056 marital-status 
     40.049 relationship 
    1050.022 race 
    11 0.018 hours-per-week 
    12 0.016 capital-gain 
    13 0.011 native-country 
    14 0.010 capital-loss 
    15 -0.003 fnlwgt 
     60.019 capital-gain 
     70.012 education-num 
     80.002 capital-loss 
     90.001 hours-per-week 
     10-0.002 age 
     11-0.006 native-country 
     12-0.011 occupation 
     13-0.014 education 
     14-0.022 fnlwgt 
     15-0.025 sex 
    1616 
    17 After feature subset selection with margin 0.010 (12 attributes): 
    18 0.275 marital-status 
    19 0.222 relationship 
    20 0.123 education 
    21 0.059 education-num 
    22 0.059 workclass 
    23 0.054 occupation 
    24 0.040 sex 
    25 0.022 age 
    26 0.019 hours-per-week 
    27 0.016 capital-gain 
    28 0.015 race 
    29 0.010 capital-loss 
     17After feature subset selection with margin 0.010 (6 attributes): 
     180.107 workclass 
     190.093 relationship 
     200.065 marital-status 
     210.021 capital-gain 
     220.020 race 
     230.018 education-num 
  • testing/regressionTests/results/orange/reference/MeasureAttribute1a.py.txt

    r5824 r9307  
    1 1.69598531723 
    2 1.100: -0.000 
    3 1.200: -0.001 
    4 1.300: 0.054 
    5 1.400: 0.131 
    6 1.500: 0.303 
    7 1.600: 0.449 
    8 1.700: 0.665 
    9 1.900: 0.665 
    10 3.000: 0.652 
    11 3.300: 0.624 
    12 3.500: 0.602 
    13 3.600: 0.580 
    14 3.700: 0.577 
    15 3.800: 0.578 
    16 3.900: 0.572 
    17 4.000: 0.563 
    18 4.100: 0.548 
    19 4.200: 0.540 
    20 4.300: 0.536 
    21 4.400: 0.535 
    22 4.500: 0.486 
    23 4.600: 0.486 
    24 4.700: 0.481 
    25 4.800: 0.427 
    26 4.900: 0.358 
    27 5.000: 0.342 
    28 5.100: 0.256 
    29 5.200: 0.220 
    30 5.300: 0.213 
    31 5.400: 0.188 
    32 5.500: 0.178 
    33 5.600: 0.130 
    34 5.700: 0.090 
    35 5.800: 0.071 
    36 5.900: 0.063 
    37 6.000: 0.054 
    38 6.100: 0.053 
    39 6.300: 0.033 
    40 6.600: 0.023 
    41 6.700: -0.000 
    42 6.900: -0.000 
     10.793992996216 
     21.100: 0.005 
     31.200: 0.015 
     41.300: 0.027 
     51.400: 0.052 
     61.500: 0.122 
     71.600: 0.218 
     81.700: 0.326 
     91.900: 0.328 
     103.000: 0.321 
     113.300: 0.308 
     123.500: 0.298 
     133.600: 0.288 
     143.700: 0.288 
     153.800: 0.288 
     163.900: 0.285 
     174.000: 0.279 
     184.100: 0.272 
     194.200: 0.265 
     204.300: 0.262 
     214.400: 0.257 
     224.500: 0.238 
     234.600: 0.235 
     244.700: 0.232 
     254.800: 0.206 
     264.900: 0.166 
     275.000: 0.153 
     285.100: 0.107 
     295.200: 0.099 
     305.300: 0.096 
     315.400: 0.091 
     325.500: 0.087 
     335.600: 0.067 
     345.700: 0.055 
     355.800: 0.043 
     365.900: 0.032 
     376.000: 0.032 
     386.100: 0.015 
     396.300: 0.015 
     406.400: 0.009 
     416.600: 0.010 
     426.700: 0.005 
     436.900: 0.000 
    4344 
    44 Best threshold: 2.450 (score 0.665) 
     45Best threshold: 2.450 (score 0.328) 
  • testing/regressionTests/results/orange/reference/MeasureAttribute2.py.txt

    r5824 r9307  
    2323 
    2424Relief 
    25                 - no unknowns:         1.0000        -0.5833        -0.1667         0.0833 
    26               - with unknowns:         0.7580        -0.3412         0.0256         0.2157 
     25                - no unknowns:         1.0000        -0.6250        -0.1667         0.0833 
     26              - with unknowns:         0.7732        -0.3676         0.0385         0.1575 
    2727 
    2828Cost matrix ((0, 5), (1, 0)) 
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