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  • Orange/testing/regression/results_modules/tree8.py.txt

    r9689 r9834  
    11m = 0.000: 239 nodes, 134 leaves 
    2 m = 0.000: 228 nodes, 128 leaves 
     2m = 0.000: 183 nodes, 104 leaves 
    33m = 0.100: 173 nodes, 99 leaves 
    44m = 0.500: 179 nodes, 102 leaves 
  • Orange/testing/regression/results_modules/tuning1.py.txt

    r9689 r9834  
    1 *** optimization  1: [0.97058829757682885]: 
    2 *** optimization  2: [0.97642948164590782]: 
    3 *** optimization  3: [0.98338801755375926]: 
    4 *** optimization  4: [0.98788177744892502]: 
    5 *** optimization  5: [0.98894238973780235]: 
    6 *** optimization  10: [0.98692031923626455]: 
    7 *** optimization  15: [0.98842239138206578]: 
    8 *** optimization  20: [0.97804067310171638]: 
     1*** optimization  1: [0.9706992853681718]: 
     2*** optimization  2: [0.9743207136103917]: 
     3*** optimization  3: [0.9833880175537593]: 
     4*** optimization  4: [0.987881777448925]: 
     5*** optimization  5: [0.9889423897378024]: 
     6*** optimization  10: [0.9869203192362646]: 
     7*** optimization  15: [0.9884223913820658]: 
     8*** optimization  20: [0.9780406731017164]: 
    99*** Optimal parameter: minSubset = 5 
    1010Optimal setting:  5 
    11 *** optimization  1: [0.98321908602150532]: 
    12 *** optimization  2: [0.97819892473118264]: 
     11*** optimization  1: [0.9832190860215053]: 
     12*** optimization  2: [0.9781989247311826]: 
    1313*** optimization  3: [0.9912679211469535]: 
    1414*** optimization  4: [0.9937656810035842]: 
    15 *** optimization  5: [0.99075044802867385]: 
     15*** optimization  5: [0.9907504480286738]: 
    1616*** optimization  10: [0.9872647849462366]: 
    17 *** optimization  15: [0.98976926523297493]: 
    18 *** optimization  20: [0.99105062724014337]: 
     17*** optimization  15: [0.9897692652329749]: 
     18*** optimization  20: [0.9910506272401434]: 
    1919*** Optimal parameter: minSubset = 4 
    20 *** optimization  1: [0.97296370967741941]: 
    21 *** optimization  2: [0.97278673835125462]: 
    22 *** optimization  3: [0.98086245519713267]: 
    23 *** optimization  4: [0.98209901433691749]: 
    24 *** optimization  5: [0.98543682795698928]: 
    25 *** optimization  10: [0.98856854838709673]: 
    26 *** optimization  15: [0.99162634408602157]: 
    27 *** optimization  20: [0.98600806451612899]: 
     20*** optimization  1: [0.9729637096774194]: 
     21*** optimization  2: [0.9727867383512546]: 
     22*** optimization  3: [0.9808624551971327]: 
     23*** optimization  4: [0.9820990143369175]: 
     24*** optimization  5: [0.9854368279569893]: 
     25*** optimization  10: [0.9885685483870967]: 
     26*** optimization  15: [0.9916263440860216]: 
     27*** optimization  20: [0.986008064516129]: 
    2828*** Optimal parameter: minSubset = 15 
    29 *** optimization  1: [0.98023073476702494]: 
    30 *** optimization  2: [0.98306899641577061]: 
    31 *** optimization  3: [0.98245295698924728]: 
     29*** optimization  1: [0.9802307347670249]: 
     30*** optimization  2: [0.9830689964157706]: 
     31*** optimization  3: [0.9824529569892473]: 
    3232*** optimization  4: [0.9896012544802868]: 
    33 *** optimization  5: [0.98472670250896055]: 
    34 *** optimization  10: [0.98965277777777783]: 
    35 *** optimization  15: [0.98743503584229386]: 
    36 *** optimization  20: [0.97437948028673826]: 
     33*** optimization  5: [0.9847267025089605]: 
     34*** optimization  10: [0.9896527777777778]: 
     35*** optimization  15: [0.9874350358422939]: 
     36*** optimization  20: [0.9743794802867383]: 
    3737*** Optimal parameter: minSubset = 10 
    38 *** optimization  1: [0.96825044802867388]: 
    39 *** optimization  2: [0.97539202508960576]: 
    40 *** optimization  3: [0.97483422939068098]: 
    41 *** optimization  4: [0.98026657706093201]: 
    42 *** optimization  5: [0.97956765232974918]: 
     38*** optimization  1: [0.9682504480286739]: 
     39*** optimization  2: [0.9763328853046596]: 
     40*** optimization  3: [0.974834229390681]: 
     41*** optimization  4: [0.980266577060932]: 
     42*** optimization  5: [0.9795676523297492]: 
    4343*** optimization  10: [0.9769332437275986]: 
    44 *** optimization  15: [0.97734543010752684]: 
     44*** optimization  15: [0.9773454301075268]: 
    4545*** optimization  20: [0.9740815412186381]: 
    4646*** Optimal parameter: minSubset = 4 
    47 *** optimization  1: [0.96364247311827955]: 
    48 *** optimization  2: [0.974209229390681]: 
    49 *** optimization  3: [0.97841621863799277]: 
    50 *** optimization  4: [0.98721102150537632]: 
    51 *** optimization  5: [0.98688396057347672]: 
    52 *** optimization  10: [0.98780689964157697]: 
    53 *** optimization  15: [0.98020833333333335]: 
    54 *** optimization  20: [0.97671370967741944]: 
     47*** optimization  1: [0.9640591397849462]: 
     48*** optimization  2: [0.9741397849462365]: 
     49*** optimization  3: [0.9783467741935483]: 
     50*** optimization  4: [0.9872110215053763]: 
     51*** optimization  5: [0.9868839605734767]: 
     52*** optimization  10: [0.987806899641577]: 
     53*** optimization  15: [0.9802083333333333]: 
     54*** optimization  20: [0.9767137096774194]: 
    5555*** Optimal parameter: minSubset = 10 
    56 *** optimization  1: [0.97435707885304657]: 
    57 *** optimization  2: [0.97433705471435883]: 
    58 *** optimization  3: [0.97782107197717805]: 
    59 *** optimization  4: [0.97559295223465736]: 
    60 *** optimization  5: [0.98345644978421476]: 
    61 *** optimization  10: [0.98451827774120404]: 
    62 *** optimization  15: [0.98160682283666145]: 
     56*** optimization  1: [0.9743570788530466]: 
     57*** optimization  2: [0.9743370547143588]: 
     58*** optimization  3: [0.977821071977178]: 
     59*** optimization  4: [0.9755929522346574]: 
     60*** optimization  5: [0.9834564497842148]: 
     61*** optimization  10: [0.984518277741204]: 
     62*** optimization  15: [0.9816068228366615]: 
    6363*** optimization  20: [0.9802781892326824]: 
    6464*** Optimal parameter: minSubset = 10 
    6565*** optimization  1: [0.9764701740911419]: 
    66 *** optimization  2: [0.98519868700168234]: 
    67 *** optimization  3: [0.98775030173359668]: 
    68 *** optimization  4: [0.98942574610489364]: 
    69 *** optimization  5: [0.98909228476336764]: 
    70 *** optimization  10: [0.98262768817204293]: 
    71 *** optimization  15: [0.98151337685611872]: 
    72 *** optimization  20: [0.98251312083973374]: 
     66*** optimization  2: [0.9851986870016823]: 
     67*** optimization  3: [0.9877503017335967]: 
     68*** optimization  4: [0.9894257461048936]: 
     69*** optimization  5: [0.9890922847633676]: 
     70*** optimization  10: [0.9826276881720429]: 
     71*** optimization  15: [0.9815133768561187]: 
     72*** optimization  20: [0.9825131208397337]: 
    7373*** Optimal parameter: minSubset = 4 
    74 *** optimization  1: [0.98090168056469895]: 
    75 *** optimization  2: [0.99166556945358786]: 
    76 *** optimization  3: [0.98834444261575594]: 
    77 *** optimization  4: [0.98837159863945589]: 
    78 *** optimization  5: [0.99087895911052581]: 
    79 *** optimization  10: [0.98963769109794453]: 
    80 *** optimization  15: [0.98902969790066564]: 
     74*** optimization  1: [0.980901680564699]: 
     75*** optimization  2: [0.9916655694535879]: 
     76*** optimization  3: [0.9883444426157559]: 
     77*** optimization  4: [0.9884388029405311]: 
     78*** optimization  5: [0.9908789591105258]: 
     79*** optimization  10: [0.9896376910979445]: 
     80*** optimization  15: [0.9890296979006656]: 
    8181*** optimization  20: [0.9841350760734402]: 
    8282*** Optimal parameter: minSubset = 2 
    83 *** optimization  1: [0.97177195340501799]: 
    84 *** optimization  2: [0.97940412186379944]: 
    85 *** optimization  3: [0.98024641577060934]: 
    86 *** optimization  4: [0.98027105734767028]: 
    87 *** optimization  5: [0.98310707885304649]: 
    88 *** optimization  10: [0.98413978494623655]: 
    89 *** optimization  15: [0.98651209677419371]: 
    90 *** optimization  20: [0.97995519713261658]: 
     83*** optimization  1: [0.971771953405018]: 
     84*** optimization  2: [0.9794041218637994]: 
     85*** optimization  3: [0.9802464157706093]: 
     86*** optimization  4: [0.9802710573476703]: 
     87*** optimization  5: [0.9831070788530465]: 
     88*** optimization  10: [0.9841397849462366]: 
     89*** optimization  15: [0.9865120967741937]: 
     90*** optimization  20: [0.9799551971326166]: 
    9191*** Optimal parameter: minSubset = 15 
    92 *** optimization  1: [0.97084229390681009]: 
    93 *** optimization  2: [0.97555107526881724]: 
    94 *** optimization  3: [0.98384184587813628]: 
    95 *** optimization  4: [0.97722222222222244]: 
    96 *** optimization  5: [0.98428539426523298]: 
     92*** optimization  1: [0.9708422939068101]: 
     93*** optimization  2: [0.9755510752688172]: 
     94*** optimization  3: [0.9838418458781363]: 
     95*** optimization  4: [0.9772222222222224]: 
     96*** optimization  5: [0.984285394265233]: 
    9797*** optimization  10: [0.989247311827957]: 
    9898*** optimization  15: [0.987780017921147]: 
    99 *** optimization  20: [0.98074148745519696]: 
     99*** optimization  20: [0.980741487455197]: 
    100100*** Optimal parameter: minSubset = 10 
    101 Untuned tree: 0.925 
     101Untuned tree: 0.926 
    102102Tuned tree: 0.983 
    103 *** optimization  1: [0.97058829757682885]: 
    104 *** optimization  2: [0.97642948164590782]: 
    105 *** optimization  3: [0.98338801755375926]: 
    106 *** optimization  4: [0.98788177744892502]: 
    107 *** optimization  5: [0.98894238973780235]: 
    108 *** optimization  10: [0.98692031923626455]: 
    109 *** optimization  15: [0.98842239138206578]: 
    110 *** optimization  20: [0.97804067310171638]: 
     103*** optimization  1: [0.9706992853681718]: 
     104*** optimization  2: [0.9743207136103917]: 
     105*** optimization  3: [0.9833880175537593]: 
     106*** optimization  4: [0.987881777448925]: 
     107*** optimization  5: [0.9889423897378024]: 
     108*** optimization  10: [0.9869203192362646]: 
     109*** optimization  15: [0.9884223913820658]: 
     110*** optimization  20: [0.9780406731017164]: 
    111111*** Optimal parameter: ['split.continuousSplitConstructor.minSubset', 'split.discreteSplitConstructor.minSubset'] = 5 
    112112Optimal setting:  5.0 
  • Orange/testing/regression/results_ofb/bagging_test.py.linux2.txt

    r9795 r9833  
    1 tree: 0.795 
    2 bagged classifier: 0.802 
     1tree: 0.733 
     2bagged classifier: 0.733 
  • Orange/testing/regression/results_ofb/data_characteristics.py.txt

    r9689 r9833  
    11Classes: 2 
    22Attributes: 14 , 6 continuous, 8 discrete 
    3 Instances:  977 total , 236 with class >50K , 741 with class <=50K 
     3Instances:  30 total , 8 with class >50K , 22 with class <=50K 
  • Orange/testing/regression/results_ofb/data_characteristics2.py.txt

    r9689 r9833  
    11Classes: 2 
    22Attributes: 14 , 6 continuous, 8 discrete 
    3 Instances:  977 total , 236(24.2%) with class >50K , 741(75.8%) with class <=50K 
     3Instances:  30 total , 8(26.7%) with class >50K , 22(73.3%) with class <=50K 
  • Orange/testing/regression/results_ofb/data_characteristics3.py.txt

    r9689 r9833  
    11Continuous attributes: 
    2   age, mean=38.91 
    3   fnlwgt, mean=187280.26 
    4   education-num, mean=9.98 
    5   capital-gain, mean=1041.83 
    6   capital-loss, mean=96.18 
    7   hours-per-week, mean=39.91 
     2  age, mean=39.63 
     3  fnlwgt, mean=198892.33 
     4  education-num, mean=10.63 
     5  capital-gain, mean=378.40 
     6  capital-loss, mean=207.73 
     7  hours-per-week, mean=39.87 
    88 
    99Nominal attributes (contingency matrix for classes: <>50K, <=50K> ) 
    1010  workclass: 
    11     Private, total 685, <147.000, 538.000> 
    12     Self-emp-not-inc, total 72, <23.000, 49.000> 
    13     Self-emp-inc, total 28, <11.000, 17.000> 
    14     Federal-gov, total 29, <15.000, 14.000> 
    15     Local-gov, total 59, <18.000, 41.000> 
    16     State-gov, total 43, <15.000, 28.000> 
    17     Without-pay, total 2, <0.000, 2.000> 
     11    Private, total 22, <4.000, 18.000> 
     12    Self-emp-not-inc, total 1, <1.000, 0.000> 
     13    Self-emp-inc, total 1, <0.000, 1.000> 
     14    Federal-gov, total 1, <1.000, 0.000> 
     15    Local-gov, total 1, <1.000, 0.000> 
     16    State-gov, total 1, <1.000, 0.000> 
     17    Without-pay, total 0, <0.000, 0.000> 
    1818    Never-worked, total 0, <0.000, 0.000> 
    1919 
    2020  education: 
    21     Bachelors, total 152, <69.000, 83.000> 
    22     Some-college, total 193, <33.000, 160.000> 
    23     11th, total 34, <2.000, 32.000> 
    24     HS-grad, total 327, <61.000, 266.000> 
    25     Prof-school, total 9, <7.000, 2.000> 
    26     Assoc-acdm, total 39, <13.000, 26.000> 
    27     Assoc-voc, total 41, <5.000, 36.000> 
    28     9th, total 18, <1.000, 17.000> 
    29     7th-8th, total 30, <2.000, 28.000> 
    30     12th, total 22, <0.000, 22.000> 
    31     Masters, total 57, <33.000, 24.000> 
    32     1st-4th, total 8, <1.000, 7.000> 
    33     10th, total 24, <1.000, 23.000> 
    34     Doctorate, total 15, <7.000, 8.000> 
    35     5th-6th, total 8, <1.000, 7.000> 
     21    Bachelors, total 6, <2.000, 4.000> 
     22    Some-college, total 6, <1.000, 5.000> 
     23    11th, total 0, <0.000, 0.000> 
     24    HS-grad, total 7, <0.000, 7.000> 
     25    Prof-school, total 0, <0.000, 0.000> 
     26    Assoc-acdm, total 3, <2.000, 1.000> 
     27    Assoc-voc, total 1, <0.000, 1.000> 
     28    9th, total 1, <0.000, 1.000> 
     29    7th-8th, total 2, <0.000, 2.000> 
     30    12th, total 0, <0.000, 0.000> 
     31    Masters, total 3, <3.000, 0.000> 
     32    1st-4th, total 0, <0.000, 0.000> 
     33    10th, total 0, <0.000, 0.000> 
     34    Doctorate, total 1, <0.000, 1.000> 
     35    5th-6th, total 0, <0.000, 0.000> 
    3636    Preschool, total 0, <0.000, 0.000> 
    3737 
    3838  marital-status: 
    39     Married-civ-spouse, total 452, <201.000, 251.000> 
    40     Divorced, total 132, <11.000, 121.000> 
    41     Never-married, total 297, <16.000, 281.000> 
    42     Separated, total 41, <3.000, 38.000> 
    43     Widowed, total 37, <4.000, 33.000> 
    44     Married-spouse-absent, total 17, <1.000, 16.000> 
    45     Married-AF-spouse, total 1, <0.000, 1.000> 
     39    Married-civ-spouse, total 12, <7.000, 5.000> 
     40    Divorced, total 4, <0.000, 4.000> 
     41    Never-married, total 8, <0.000, 8.000> 
     42    Separated, total 3, <0.000, 3.000> 
     43    Widowed, total 3, <1.000, 2.000> 
     44    Married-spouse-absent, total 0, <0.000, 0.000> 
     45    Married-AF-spouse, total 0, <0.000, 0.000> 
    4646 
    4747  occupation: 
    48     Tech-support, total 33, <13.000, 20.000> 
    49     Craft-repair, total 117, <30.000, 87.000> 
    50     Other-service, total 103, <1.000, 102.000> 
    51     Sales, total 92, <31.000, 61.000> 
    52     Exec-managerial, total 125, <53.000, 72.000> 
    53     Prof-specialty, total 110, <44.000, 66.000> 
    54     Handlers-cleaners, total 40, <3.000, 37.000> 
    55     Machine-op-inspct, total 62, <7.000, 55.000> 
    56     Adm-clerical, total 115, <20.000, 95.000> 
    57     Farming-fishing, total 29, <5.000, 24.000> 
    58     Transport-moving, total 61, <16.000, 45.000> 
    59     Priv-house-serv, total 10, <0.000, 10.000> 
    60     Protective-serv, total 20, <6.000, 14.000> 
    61     Armed-Forces, total 1, <0.000, 1.000> 
     48    Tech-support, total 1, <0.000, 1.000> 
     49    Craft-repair, total 3, <1.000, 2.000> 
     50    Other-service, total 3, <0.000, 3.000> 
     51    Sales, total 4, <1.000, 3.000> 
     52    Exec-managerial, total 7, <3.000, 4.000> 
     53    Prof-specialty, total 5, <3.000, 2.000> 
     54    Handlers-cleaners, total 0, <0.000, 0.000> 
     55    Machine-op-inspct, total 0, <0.000, 0.000> 
     56    Adm-clerical, total 2, <0.000, 2.000> 
     57    Farming-fishing, total 0, <0.000, 0.000> 
     58    Transport-moving, total 2, <0.000, 2.000> 
     59    Priv-house-serv, total 0, <0.000, 0.000> 
     60    Protective-serv, total 0, <0.000, 0.000> 
     61    Armed-Forces, total 0, <0.000, 0.000> 
    6262 
    6363  relationship: 
    64     Wife, total 63, <33.000, 30.000> 
    65     Own-child, total 146, <3.000, 143.000> 
    66     Husband, total 384, <166.000, 218.000> 
    67     Not-in-family, total 249, <25.000, 224.000> 
    68     Other-relative, total 27, <2.000, 25.000> 
    69     Unmarried, total 108, <7.000, 101.000> 
     64    Wife, total 1, <1.000, 0.000> 
     65    Own-child, total 3, <0.000, 3.000> 
     66    Husband, total 11, <6.000, 5.000> 
     67    Not-in-family, total 8, <0.000, 8.000> 
     68    Other-relative, total 0, <0.000, 0.000> 
     69    Unmarried, total 7, <1.000, 6.000> 
    7070 
    7171  race: 
    72     White, total 825, <214.000, 611.000> 
    73     Asian-Pac-Islander, total 32, <8.000, 24.000> 
    74     Amer-Indian-Eskimo, total 8, <0.000, 8.000> 
    75     Other, total 12, <1.000, 11.000> 
    76     Black, total 100, <13.000, 87.000> 
     72    White, total 27, <7.000, 20.000> 
     73    Asian-Pac-Islander, total 0, <0.000, 0.000> 
     74    Amer-Indian-Eskimo, total 0, <0.000, 0.000> 
     75    Other, total 0, <0.000, 0.000> 
     76    Black, total 3, <1.000, 2.000> 
    7777 
    7878  sex: 
    79     Female, total 353, <51.000, 302.000> 
    80     Male, total 624, <185.000, 439.000> 
     79    Female, total 13, <2.000, 11.000> 
     80    Male, total 17, <6.000, 11.000> 
    8181 
    8282  native-country: 
    83     United-States, total 864, <215.000, 649.000> 
    84     Cuba, total 1, <0.000, 1.000> 
    85     Jamaica, total 2, <1.000, 1.000> 
    86     India, total 3, <1.000, 2.000> 
    87     Mexico, total 25, <2.000, 23.000> 
    88     South, total 1, <0.000, 1.000> 
    89     Puerto-Rico, total 1, <0.000, 1.000> 
     83    United-States, total 29, <8.000, 21.000> 
     84    Cuba, total 0, <0.000, 0.000> 
     85    Jamaica, total 0, <0.000, 0.000> 
     86    India, total 0, <0.000, 0.000> 
     87    Mexico, total 0, <0.000, 0.000> 
     88    South, total 0, <0.000, 0.000> 
     89    Puerto-Rico, total 0, <0.000, 0.000> 
    9090    Honduras, total 0, <0.000, 0.000> 
    91     England, total 4, <0.000, 4.000> 
    92     Canada, total 5, <1.000, 4.000> 
    93     Germany, total 7, <2.000, 5.000> 
    94     Iran, total 1, <0.000, 1.000> 
    95     Philippines, total 4, <1.000, 3.000> 
    96     Italy, total 3, <1.000, 2.000> 
    97     Poland, total 2, <0.000, 2.000> 
    98     Columbia, total 2, <1.000, 1.000> 
    99     Cambodia, total 1, <0.000, 1.000> 
     91    England, total 1, <0.000, 1.000> 
     92    Canada, total 0, <0.000, 0.000> 
     93    Germany, total 0, <0.000, 0.000> 
     94    Iran, total 0, <0.000, 0.000> 
     95    Philippines, total 0, <0.000, 0.000> 
     96    Italy, total 0, <0.000, 0.000> 
     97    Poland, total 0, <0.000, 0.000> 
     98    Columbia, total 0, <0.000, 0.000> 
     99    Cambodia, total 0, <0.000, 0.000> 
    100100    Thailand, total 0, <0.000, 0.000> 
    101101    Ecuador, total 0, <0.000, 0.000> 
    102     Laos, total 1, <0.000, 1.000> 
    103     Taiwan, total 4, <1.000, 3.000> 
    104     Haiti, total 2, <1.000, 1.000> 
     102    Laos, total 0, <0.000, 0.000> 
     103    Taiwan, total 0, <0.000, 0.000> 
     104    Haiti, total 0, <0.000, 0.000> 
    105105    Portugal, total 0, <0.000, 0.000> 
    106     Dominican-Republic, total 1, <0.000, 1.000> 
    107     El-Salvador, total 3, <0.000, 3.000> 
     106    Dominican-Republic, total 0, <0.000, 0.000> 
     107    El-Salvador, total 0, <0.000, 0.000> 
    108108    France, total 0, <0.000, 0.000> 
    109     Guatemala, total 1, <0.000, 1.000> 
    110     China, total 3, <1.000, 2.000> 
    111     Japan, total 1, <1.000, 0.000> 
     109    Guatemala, total 0, <0.000, 0.000> 
     110    China, total 0, <0.000, 0.000> 
     111    Japan, total 0, <0.000, 0.000> 
    112112    Yugoslavia, total 0, <0.000, 0.000> 
    113     Peru, total 1, <0.000, 1.000> 
    114     Outlying-US(Guam-USVI-etc), total 1, <0.000, 1.000> 
    115     Scotland, total 1, <0.000, 1.000> 
     113    Peru, total 0, <0.000, 0.000> 
     114    Outlying-US(Guam-USVI-etc), total 0, <0.000, 0.000> 
     115    Scotland, total 0, <0.000, 0.000> 
    116116    Trinadad&Tobago, total 0, <0.000, 0.000> 
    117     Greece, total 1, <0.000, 1.000> 
    118     Nicaragua, total 2, <0.000, 2.000> 
    119     Vietnam, total 3, <0.000, 3.000> 
     117    Greece, total 0, <0.000, 0.000> 
     118    Nicaragua, total 0, <0.000, 0.000> 
     119    Vietnam, total 0, <0.000, 0.000> 
    120120    Hong, total 0, <0.000, 0.000> 
    121     Ireland, total 3, <1.000, 2.000> 
     121    Ireland, total 0, <0.000, 0.000> 
    122122    Hungary, total 0, <0.000, 0.000> 
    123123    Holand-Netherlands, total 0, <0.000, 0.000> 
  • Orange/testing/regression/results_ofb/data_characteristics4.py.txt

    r9689 r9833  
    11Average values and mean square errors: 
    2 age, mean=38.91 +-  0.44 
    3 fnlwgt, mean=187280.29 +- 3464.37 
    4 education-num, mean= 9.98 +-  0.08 
    5 capital-gain, mean=1041.83 +- 218.78 
    6 capital-loss, mean=96.18 +- 13.68 
    7 hours-per-week, mean=39.91 +-  0.41 
     2age, mean=39.63 +-  2.75 
     3fnlwgt, mean=198892.33 +- 19629.05 
     4education-num, mean=10.63 +-  0.53 
     5capital-gain, mean=378.40 +- 201.02 
     6capital-loss, mean=207.73 +- 156.10 
     7hours-per-week, mean=39.87 +-  2.34 
    88 
    99Frequencies for values of discrete attributes: 
    1010workclass: 
    11   Private: 685 
    12   Self-emp-not-inc: 72 
    13   Self-emp-inc: 28 
    14   Federal-gov: 29 
    15   Local-gov: 59 
    16   State-gov: 43 
    17   Without-pay: 2 
     11  Private: 22 
     12  Self-emp-not-inc: 1 
     13  Self-emp-inc: 1 
     14  Federal-gov: 1 
     15  Local-gov: 1 
     16  State-gov: 1 
     17  Without-pay: 0 
    1818  Never-worked: 0 
    1919education: 
    20   Bachelors: 152 
    21   Some-college: 193 
    22   11th: 34 
    23   HS-grad: 327 
    24   Prof-school: 9 
    25   Assoc-acdm: 39 
    26   Assoc-voc: 41 
    27   9th: 18 
    28   7th-8th: 30 
    29   12th: 22 
    30   Masters: 57 
    31   1st-4th: 8 
    32   10th: 24 
    33   Doctorate: 15 
    34   5th-6th: 8 
     20  Bachelors: 6 
     21  Some-college: 6 
     22  11th: 0 
     23  HS-grad: 7 
     24  Prof-school: 0 
     25  Assoc-acdm: 3 
     26  Assoc-voc: 1 
     27  9th: 1 
     28  7th-8th: 2 
     29  12th: 0 
     30  Masters: 3 
     31  1st-4th: 0 
     32  10th: 0 
     33  Doctorate: 1 
     34  5th-6th: 0 
    3535  Preschool: 0 
    3636marital-status: 
    37   Married-civ-spouse: 452 
    38   Divorced: 132 
    39   Never-married: 297 
    40   Separated: 41 
    41   Widowed: 37 
    42   Married-spouse-absent: 17 
    43   Married-AF-spouse: 1 
     37  Married-civ-spouse: 12 
     38  Divorced: 4 
     39  Never-married: 8 
     40  Separated: 3 
     41  Widowed: 3 
     42  Married-spouse-absent: 0 
     43  Married-AF-spouse: 0 
    4444occupation: 
    45   Tech-support: 33 
    46   Craft-repair: 117 
    47   Other-service: 103 
    48   Sales: 92 
    49   Exec-managerial: 125 
    50   Prof-specialty: 110 
    51   Handlers-cleaners: 40 
    52   Machine-op-inspct: 62 
    53   Adm-clerical: 115 
    54   Farming-fishing: 29 
    55   Transport-moving: 61 
    56   Priv-house-serv: 10 
    57   Protective-serv: 20 
    58   Armed-Forces: 1 
     45  Tech-support: 1 
     46  Craft-repair: 3 
     47  Other-service: 3 
     48  Sales: 4 
     49  Exec-managerial: 7 
     50  Prof-specialty: 5 
     51  Handlers-cleaners: 0 
     52  Machine-op-inspct: 0 
     53  Adm-clerical: 2 
     54  Farming-fishing: 0 
     55  Transport-moving: 2 
     56  Priv-house-serv: 0 
     57  Protective-serv: 0 
     58  Armed-Forces: 0 
    5959relationship: 
    60   Wife: 63 
    61   Own-child: 146 
    62   Husband: 384 
    63   Not-in-family: 249 
    64   Other-relative: 27 
    65   Unmarried: 108 
     60  Wife: 1 
     61  Own-child: 3 
     62  Husband: 11 
     63  Not-in-family: 8 
     64  Other-relative: 0 
     65  Unmarried: 7 
    6666race: 
    67   White: 825 
    68   Asian-Pac-Islander: 32 
    69   Amer-Indian-Eskimo: 8 
    70   Other: 12 
    71   Black: 100 
     67  White: 27 
     68  Asian-Pac-Islander: 0 
     69  Amer-Indian-Eskimo: 0 
     70  Other: 0 
     71  Black: 3 
    7272sex: 
    73   Female: 353 
    74   Male: 624 
     73  Female: 13 
     74  Male: 17 
    7575native-country: 
    76   United-States: 864 
    77   Cuba: 1 
    78   Jamaica: 2 
    79   India: 3 
    80   Mexico: 25 
    81   South: 1 
    82   Puerto-Rico: 1 
     76  United-States: 29 
     77  Cuba: 0 
     78  Jamaica: 0 
     79  India: 0 
     80  Mexico: 0 
     81  South: 0 
     82  Puerto-Rico: 0 
    8383  Honduras: 0 
    84   England: 4 
    85   Canada: 5 
    86   Germany: 7 
    87   Iran: 1 
    88   Philippines: 4 
    89   Italy: 3 
    90   Poland: 2 
    91   Columbia: 2 
    92   Cambodia: 1 
     84  England: 1 
     85  Canada: 0 
     86  Germany: 0 
     87  Iran: 0 
     88  Philippines: 0 
     89  Italy: 0 
     90  Poland: 0 
     91  Columbia: 0 
     92  Cambodia: 0 
    9393  Thailand: 0 
    9494  Ecuador: 0 
    95   Laos: 1 
    96   Taiwan: 4 
    97   Haiti: 2 
     95  Laos: 0 
     96  Taiwan: 0 
     97  Haiti: 0 
    9898  Portugal: 0 
    99   Dominican-Republic: 1 
    100   El-Salvador: 3 
     99  Dominican-Republic: 0 
     100  El-Salvador: 0 
    101101  France: 0 
    102   Guatemala: 1 
    103   China: 3 
    104   Japan: 1 
     102  Guatemala: 0 
     103  China: 0 
     104  Japan: 0 
    105105  Yugoslavia: 0 
    106   Peru: 1 
    107   Outlying-US(Guam-USVI-etc): 1 
    108   Scotland: 1 
     106  Peru: 0 
     107  Outlying-US(Guam-USVI-etc): 0 
     108  Scotland: 0 
    109109  Trinadad&Tobago: 0 
    110   Greece: 1 
    111   Nicaragua: 2 
    112   Vietnam: 3 
     110  Greece: 0 
     111  Nicaragua: 0 
     112  Vietnam: 0 
    113113  Hong: 0 
    114   Ireland: 3 
     114  Ireland: 0 
    115115  Hungary: 0 
    116116  Holand-Netherlands: 0 
     
    118118Number of instances where attribute is not defined: 
    119119   0 age 
    120   59 workclass 
     120   3 workclass 
    121121   0 fnlwgt 
    122122   0 education 
    123123   0 education-num 
    124124   0 marital-status 
    125   59 occupation 
     125   3 occupation 
    126126   0 relationship 
    127127   0 race 
     
    130130   0 capital-loss 
    131131   0 hours-per-week 
    132   23 native-country 
     132   0 native-country 
  • Orange/testing/regression/results_ofb/domain4.py.txt

    r9689 r9833  
    1 Size of original data set: 977 instances 
    2 p(>50K)=0.242 
    3 Size of data1: 624 instances 
    4 p(>50K)=0.296 
    5 Size of data2: 38 instances 
    6 p(>50K)=0.632 
     1Size of original data set: 30 instances 
     2p(>50K)=0.267 
     3Size of data1: 17 instances 
     4p(>50K)=0.353 
     5Size of data2: 1 instances 
     6p(>50K)=1.000 
  • Orange/testing/regression/results_ofb/domain5.py.txt

    r9689 r9833  
    1 Size of data: 977 instances;  p(>50K)=0.242 
    2 Size of data1 (conjunction): 38 instances;  p(>50K)=0.632 
    3 Size of data1 (disjunction): 643 instances;  p(>50K)=0.302 
    4 Size of data2 (select, conjuction): 38 instances;  p(>50K)=0.632 
     1Size of data: 30 instances;  p(>50K)=0.267 
     2Size of data1 (conjunction): 1 instances;  p(>50K)=1.000 
     3Size of data1 (disjunction): 19 instances;  p(>50K)=0.421 
     4Size of data2 (select, conjuction): 1 instances;  p(>50K)=1.000 
  • Orange/testing/regression/results_ofb/domain6.py.txt

    r9689 r9833  
    1 Size of data: 977 instances;  p(>50K)=0.242 
    2 Size of data1, age from 30 to 40: 301 instances;  p(>50K)=0.312 
    3 Size of data2, younger than 30 or older than 40: 676 instances;  p(>50K)=0.210 
     1Size of data: 30 instances;  p(>50K)=0.267 
     2Size of data1, age from 30 to 40: 13 instances;  p(>50K)=0.231 
     3Size of data2, younger than 30 or older than 40: 17 instances;  p(>50K)=0.294 
  • Orange/testing/regression/results_orange25/simple_tree_random_forest.py.txt

    r9802 r9830  
    44 
    55Runtimes: 
    6 for_gain 0.0934960842133 
    7 for_simp 0.0251078605652 
     6for_gain 0.0932638645172 
     7for_simp 0.0247650146484 
  • Orange/testing/regression/results_orange25/svm-linear-weights.py.txt

    r9804 r9831  
    1 defaultdict(<type 'float'>, {FloatVariable 'Elu 30': 0.4786304184632838, FloatVariable 'spo 0': 0.13024372486415015, FloatVariable 'Elu 60': 0.36698713537474476, FloatVariable 'spo 2': 0.7078062232168753, FloatVariable 'Elu 90': 0.3834942300173535, FloatVariable 'spo 5': 1.0683498605674933, FloatVariable 'alpha 7': 0.06557659077448137, FloatVariable 'Elu 120': 0.5939764872379445, FloatVariable 'spo 7': 0.8081568079276176, FloatVariable 'diau d': 0.44932601596409105, FloatVariable 'Elu 150': 0.5965599387054419, FloatVariable 'alpha 119': 0.1699365258012951, FloatVariable 'spo 9': 0.2498282401589412, FloatVariable 'Elu 180': 0.42425268429856355, FloatVariable 'spo 11': 0.20615575833508282, FloatVariable 'alpha 70': 0.26459268873021585, FloatVariable 'Elu 210': 0.12396520046361383, FloatVariable 'spo5 2': 0.40417556232809326, FloatVariable 'Elu 240': 0.2093390824178926, FloatVariable 'spo5 7': 0.26780459416067937, FloatVariable 'Elu 270': 0.33471969574325466, FloatVariable 'alpha 84': 0.1308234316119738, FloatVariable 'spo5 11': 1.200079459496442, FloatVariable 'diau e': 0.864767371223623, FloatVariable 'Elu 300': 0.15913983311663107, FloatVariable 'spo- early': 1.9466556509082333, FloatVariable 'Elu 330': 0.11474308886955724, FloatVariable 'alpha 42': 0.13641650791763626, FloatVariable 'spo- mid': 3.2086605964825132, FloatVariable 'Elu 360': 0.16577258493775038, FloatVariable 'alpha 14': 0.18310901108005986, FloatVariable 'Elu 390': 0.1820761083768519, FloatVariable 'alpha 21': 0.030539018557891578, FloatVariable 'cdc15 10': 0.11428450056762224, FloatVariable 'alpha 28': 0.04645238275160125, FloatVariable 'alpha 91': 0.1905674816738207, FloatVariable 'cdc15 30': 0.18270335600911874, FloatVariable 'alpha 35': 0.21379911334384863, FloatVariable 'heat 0': 0.19091815990337407, FloatVariable 'cdc15 50': 0.24968263583989325, FloatVariable 'cdc15 170': 0.44789694844623534, FloatVariable 'heat 80': 0.38909905042009185, FloatVariable 'diau f': 1.4452997087693935, FloatVariable 'cdc15 70': 0.13876265882583333, FloatVariable 'alpha 49': 0.16085715739160683, FloatVariable 'cdc15 90': 0.32729758144823035, FloatVariable 'alpha 56': 0.3367416107117914, FloatVariable 'cold 0': 0.27980454530046744, FloatVariable 'cdc15 110': 0.564756618474929, FloatVariable 'Elu 0': 0.8466167037587657, FloatVariable 'alpha 63': 0.18433878873311124, FloatVariable 'cdc15 130': 0.3658301477295572, FloatVariable 'dtt 60': 0.5951914850021424, FloatVariable 'alpha 105': 0.14088060621674625, FloatVariable 'cdc15 150': 0.693249161777514, FloatVariable 'dtt 120': 0.55305024494988, FloatVariable 'alpha 112': 0.19329749923741518, FloatVariable 'diau g': 2.248793727194904, FloatVariable 'heat 10': 1.000320207469925, FloatVariable 'cdc15 190': 0.16956982427965123, FloatVariable 'heat 160': 0.3192185000510415, FloatVariable 'dtt 15': 0.49451797411099035, FloatVariable 'cold 20': 0.4097605043285248, FloatVariable 'alpha 0': 0.19198054903386352, FloatVariable 'cdc15 210': 0.15183429673463036, FloatVariable 'cold 40': 0.3092287272528724, FloatVariable 'alpha 98': 0.21754881357923167, FloatVariable 'cdc15 230': 0.5474715182870784, FloatVariable 'cold 160': 0.6947037871090163, FloatVariable 'heat 40': 0.4580618143377812, FloatVariable 'cdc15 250': 0.3573777070361102, FloatVariable 'dtt 30': 0.5838556086306895, FloatVariable 'diau a': 0.14935761521655416, FloatVariable 'alpha 77': 0.20088381949723239, FloatVariable 'cdc15 270': 0.21951931922594184, FloatVariable 'diau b': 0.23473821977067888, FloatVariable 'heat 20': 0.9867456006798212, FloatVariable 'cdc15 290': 0.24965577080121784, FloatVariable 'diau c': 0.13741762346585432}) 
     1defaultdict(<type 'float'>, {FloatVariable 'Elu 30': 0.4786304184632838, FloatVariable 'spo 0': 0.13024372486415015, FloatVariable 'Elu 60': 0.36698713537474476, FloatVariable 'spo 2': 0.7078062232168753, FloatVariable 'alpha 98': 0.21754881357923167, FloatVariable 'Elu 90': 0.3834942300173535, FloatVariable 'spo 5': 1.0683498605674933, FloatVariable 'alpha 7': 0.06557659077448137, FloatVariable 'Elu 120': 0.5939764872379445, FloatVariable 'spo 7': 0.8081568079276176, FloatVariable 'diau d': 0.44932601596409105, FloatVariable 'Elu 150': 0.5965599387054419, FloatVariable 'spo 9': 0.2498282401589412, FloatVariable 'alpha 35': 0.21379911334384863, FloatVariable 'Elu 180': 0.42425268429856355, FloatVariable 'spo 11': 0.20615575833508282, FloatVariable 'alpha 70': 0.26459268873021585, FloatVariable 'Elu 210': 0.12396520046361383, FloatVariable 'spo5 2': 0.40417556232809326, FloatVariable 'alpha 63': 0.18433878873311124, FloatVariable 'Elu 240': 0.2093390824178926, FloatVariable 'spo5 7': 0.26780459416067937, FloatVariable 'Elu 270': 0.33471969574325466, FloatVariable 'spo5 11': 1.200079459496442, FloatVariable 'diau e': 0.864767371223623, FloatVariable 'spo- early': 1.9466556509082333, FloatVariable 'Elu 330': 0.11474308886955724, FloatVariable 'alpha 42': 0.13641650791763626, FloatVariable 'spo- mid': 3.2086605964825132, FloatVariable 'Elu 360': 0.16577258493775038, FloatVariable 'cdc15 270': 0.21951931922594184, FloatVariable 'alpha 14': 0.18310901108005986, FloatVariable 'Elu 390': 0.1820761083768519, FloatVariable 'alpha 21': 0.030539018557891578, FloatVariable 'cdc15 10': 0.11428450056762224, FloatVariable 'alpha 28': 0.04645238275160125, FloatVariable 'cdc15 30': 0.18270335600911874, FloatVariable 'heat 40': 0.4580618143377812, FloatVariable 'heat 0': 0.19091815990337407, FloatVariable 'cdc15 50': 0.24968263583989325, FloatVariable 'cdc15 170': 0.44789694844623534, FloatVariable 'heat 80': 0.38909905042009185, FloatVariable 'diau f': 1.4452997087693935, FloatVariable 'cdc15 70': 0.13876265882583333, FloatVariable 'alpha 49': 0.16085715739160683, FloatVariable 'cdc15 90': 0.32729758144823035, FloatVariable 'alpha 56': 0.3367416107117914, FloatVariable 'alpha 84': 0.1308234316119738, FloatVariable 'cdc15 110': 0.564756618474929, FloatVariable 'dtt 30': 0.5838556086306895, FloatVariable 'alpha 112': 0.19329749923741518, FloatVariable 'cdc15 130': 0.3658301477295572, FloatVariable 'heat 10': 1.000320207469925, FloatVariable 'dtt 60': 0.5951914850021424, FloatVariable 'cdc15 150': 0.693249161777514, FloatVariable 'dtt 120': 0.55305024494988, FloatVariable 'diau g': 2.248793727194904, FloatVariable 'cold 0': 0.27980454530046744, FloatVariable 'Elu 0': 0.8466167037587657, FloatVariable 'cdc15 190': 0.16956982427965123, FloatVariable 'alpha 77': 0.20088381949723239, FloatVariable 'cold 20': 0.4097605043285248, FloatVariable 'alpha 0': 0.19198054903386352, FloatVariable 'cdc15 210': 0.15183429673463036, FloatVariable 'cold 40': 0.3092287272528724, FloatVariable 'alpha 119': 0.1699365258012951, FloatVariable 'dtt 15': 0.49451797411099035, FloatVariable 'cdc15 230': 0.5474715182870784, FloatVariable 'diau a': 0.14935761521655416, FloatVariable 'cold 160': 0.6947037871090163, FloatVariable 'cdc15 250': 0.3573777070361102, FloatVariable 'diau c': 0.13741762346585432, FloatVariable 'heat 160': 0.3192185000510415, FloatVariable 'alpha 105': 0.14088060621674625, FloatVariable 'diau b': 0.23473821977067888, FloatVariable 'heat 20': 0.9867456006798212, FloatVariable 'cdc15 290': 0.24965577080121784, FloatVariable 'Elu 300': 0.15913983311663107, FloatVariable 'alpha 91': 0.1905674816738207}) 
  • docs/reference/rst/code/network-optimization.py

    r9823 r9839  
    99for i in range(4): 
    1010    for j in range(i + 1, 5): 
    11         net[i,j] = 1 
     11        net[i, j] = 1 
    1212 
    1313# vertices are placed randomly in NetworkOptimization constructor 
    1414networkOptimization = Orange.network.NetworkOptimization(net) 
     15 
    1516 
    1617# read all edges and plot a line 
  • docs/tutorial/rst/code/sample_adult.py

    r9747 r9832  
    99selection = orange.MakeRandomIndices2(data, 0.03) 
    1010sample = data.select(selection, 0) 
    11 sample.save("adult_sample.tab") 
     11sample.save("adult_sample_sampled.tab") 
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