| m4_include(`SQLCommon.m4') |
| |
| /* ----------------------------------------------------------------------------- |
| * Test Logistic Regression. |
| * -------------------------------------------------------------------------- */ |
| |
| /* |
| * The following example is taken from: |
| * http://luna.cas.usf.edu/~mbrannic/files/regression/Logistic.html |
| * Predicting heart attack |
| */ |
| DROP TABLE IF EXISTS patients; |
| CREATE TABLE patients ( |
| id INTEGER NOT NULL, |
| second_attack INTEGER, |
| treatment INTEGER, |
| trait_anxiety INTEGER |
| )m4_ifdef(`__POSTGRESQL__', `', `DISTRIBUTED BY (id)'); |
| |
| INSERT INTO patients(ID, second_attack, treatment, trait_anxiety) VALUES |
| ( 0, NULL, 1, 70), |
| ( 1, 1, 1, 70), |
| ( 2, 1, 1, 80), |
| ( 3, 1, 1, 50), |
| ( 4, 1, 0, 60), |
| ( 5, 1, 0, 40), |
| ( 6, 1, 0, 65), |
| ( 7, 1, 0, 75), |
| ( 8, 1, 0, 80), |
| ( 9, 1, 0, 70), |
| (10, 1, 0, 60), |
| (11, 0, 1, 65), |
| (12, 0, 1, 50), |
| (13, 0, 1, 45), |
| (14, 0, 1, 35), |
| (15, 0, 1, 40), |
| (16, 0, 1, 50), |
| (17, 0, 0, 55), |
| (18, 0, 0, 45), |
| (19, 0, 0, 50), |
| (20, 0, 0, 60), |
| (21, 0, 0, NULL), |
| (22, 0, NULL, 60); |
| |
| CREATE VIEW patients_view AS |
| (SELECT * FROM patients) ; |
| |
| drop table if exists temp_result; |
| drop table if exists temp_result_summary; |
| select logregr_train( |
| 'patients', |
| 'temp_result', |
| 'second_attack', |
| 'ARRAY[1, treatment, trait_anxiety]', |
| Null, |
| 20, |
| 'irls' |
| ); |
| |
| -- The coefficients are from the source above, the other values have been |
| -- computed with the IRLS optimizer in MADlib |
| SELECT assert( |
| relative_error(coef, ARRAY[-6.36, -1.02, 0.119]) < 1e-3 AND |
| relative_error(log_likelihood, -9.41) < 1e-3 AND |
| relative_error(std_err, ARRAY[3.21, 1.17, 0.0550]) < 0.002 AND |
| relative_error(z_stats, ARRAY[-1.98, -0.874, 2.17]) < 0.002 AND |
| relative_error(p_values, ARRAY[0.0477, 0.382, 0.0304]) < 1e-3 AND |
| relative_error(odds_ratios, ARRAY[0.00172, 0.359, 1.13]) < 0.004 AND |
| relative_error(condition_no, sqrt(106329)) < 1e-2 AND |
| num_rows_processed = 20 AND |
| num_missing_rows_skipped = 3, |
| 'Logistic regression with IRLS optimizer (patients test): Wrong results' |
| ) FROM temp_result; |
| |
| -- Run the same test as above, but use the view of patients. |
| drop table if exists temp_result; |
| drop table if exists temp_result_summary; |
| select logregr_train( |
| 'patients_view', |
| 'temp_result', |
| 'second_attack', |
| 'ARRAY[1, treatment, trait_anxiety]', |
| Null, |
| 20, |
| 'irls' |
| ); |
| |
| -- The coefficients are from the source above, the other values have been |
| -- computed with the IRLS optimizer in MADlib |
| SELECT assert( |
| relative_error(coef, ARRAY[-6.36, -1.02, 0.119]) < 1e-3 AND |
| relative_error(log_likelihood, -9.41) < 1e-3 AND |
| relative_error(std_err, ARRAY[3.21, 1.17, 0.0550]) < 0.002 AND |
| relative_error(z_stats, ARRAY[-1.98, -0.874, 2.17]) < 0.002 AND |
| relative_error(p_values, ARRAY[0.0477, 0.382, 0.0304]) < 1e-3 AND |
| relative_error(odds_ratios, ARRAY[0.00172, 0.359, 1.13]) < 0.004 AND |
| relative_error(condition_no, sqrt(106329)) < 1e-2 AND |
| num_rows_processed = 20 AND |
| num_missing_rows_skipped = 3, |
| 'Logistic regression with IRLS optimizer (patients test): Wrong results' |
| ) FROM temp_result; |
| |
| |
| drop table if exists temp_result; |
| drop table if exists temp_result_summary; |
| select logregr_train( |
| 'patients', |
| 'temp_result', |
| 'second_attack', |
| 'ARRAY[1, treatment, trait_anxiety]', |
| Null, |
| 200, |
| 'cg', |
| 0 |
| ); |
| -- We are pretty generous here |
| SELECT assert( |
| relative_error(coef, ARRAY[-6.36, -1.02, 0.119]) < 0.04 AND |
| relative_error(log_likelihood, -9.41) < 1e-3 AND |
| relative_error(std_err, ARRAY[3.21, 1.17, 0.0550]) < 0.02 AND |
| relative_error(z_stats, ARRAY[-1.98, -0.874, 2.17]) < 0.02 AND |
| relative_error(p_values, ARRAY[0.0477, 0.382, 0.0304]) < 0.03 AND |
| relative_error(odds_ratios, ARRAY[0.00172, 0.359, 1.13]) < 0.01 AND |
| relative_error(condition_no, sqrt(106329)) < 0.02 AND |
| num_rows_processed = 20 AND |
| num_missing_rows_skipped = 3, |
| 'Logistic regression (patients test): Wrong results' |
| ) |
| FROM temp_result; |
| |
| -- IGD performs poorly on this instance, so we are not testing it |
| |
| |
| |
| /* |
| * The following example is taken from: |
| * http://www.ats.ucla.edu/stat/stata/output/old/lognoframe.htm |
| * Crime data |
| * |
| * Description |
| * These data are crime-related and demographic statistics for 47 US states in |
| * 1960. The data were collected from the FBI's Uniform Crime Report and other |
| * government agencies to determine how the variable crime rate depends on the |
| * other variables measured in the study. |
| * Number of cases: 47 |
| * |
| * CrimeRat: Crime rate: # of offenses reported to police per million population |
| * MaleTeen: The number of males of age 14-24 per 1000 population |
| * South : Indicator variable for Southern states (0 = No, 1 = Yes) |
| * Educ : Mean # of years of schooling for persons of age 25 or older |
| * Police60: 1960 per capita expenditure on police by state and local government |
| * Police59: 1959 per capita expenditure on police by state and local government |
| * Labor : Labor force participation rate per 1000 civilian urban males age 14-24 |
| * Males : The number of males per 1000 females |
| * Pop : State population size in hundred thousands |
| * NonWhite: The number of non-whites per 1000 population |
| * Unemp1 : Unemployment rate of urban males per 1000 of age 14-24 |
| * Unemp2 : Unemployment rate of urban males per 1000 of age 35-39 |
| * Median : Median value of transferable goods and assets or family income in tens of $ |
| * BelowMed: The number of families per 1000 earning below 1/2 the median income |
| */ |
| DROP TABLE IF EXISTS crime; |
| CREATE TABLE crime ( |
| id SERIAL NOT NULL, |
| CrimeRat DOUBLE PRECISION, |
| MaleTeen INTEGER, |
| South SMALLINT, |
| Educ DOUBLE PRECISION, |
| Police60 INTEGER, |
| Police59 INTEGER, |
| Labor INTEGER, |
| Males INTEGER, |
| Pop INTEGER, |
| NonWhite INTEGER, |
| Unemp1 INTEGER, |
| Unemp2 INTEGER, |
| Median INTEGER, |
| BelowMed INTEGER |
| )m4_ifdef(`__POSTGRESQL__', `', `DISTRIBUTED BY (id)'); |
| |
| INSERT INTO crime( |
| CrimeRat, MaleTeen, South, Educ, Police60, Police59, Labor, Males, Pop, |
| NonWhite, Unemp1, Unemp2, Median, BelowMed |
| ) VALUES |
| (79.1, 151, 1, 9.1, 58, 56, 510, 950, 33, 301, 108, 41, 394, 261), |
| (163.5, 143, 0, 11.3, 103, 95, 583, 1012, 13, 102, 96, 36, 557, 194), |
| (57.8, 142, 1, 8.9, 45, 44, 533, 969, 18, 219, 94, 33, 318, 250), |
| (196.9, 136, 0, 12.1, 149, 141, 577, 994, 157, 80, 102, 39, 673, 167), |
| (123.4, 141, 0, 12.1, 109, 101, 591, 985, 18, 30, 91, 20, 578, 174), |
| (68.2, 121, 0, 11.0, 118, 115, 547, 964, 25, 44, 84, 29, 689, 126), |
| (96.3, 127, 1, 11.1, 82, 79, 519, 982, 4, 139, 97, 38, 620, 168), |
| (155.5, 131, 1, 10.9, 115, 109, 542, 969, 50, 179, 79, 35, 472, 206), |
| (85.6, 157, 1, 9.0, 65, 62, 553, 955, 39, 286, 81, 28, 421, 239), |
| (70.5, 140, 0, 11.8, 71, 68, 632, 1029, 7, 15, 100, 24, 526, 174), |
| (167.4, 124, 0, 10.5, 121, 116, 580, 966, 101, 106, 77, 35, 657, 170), |
| (84.9, 134, 0, 10.8, 75, 71, 595, 972, 47, 59, 83, 31, 580, 172), |
| (51.1, 128, 0, 11.3, 67, 60, 624, 972, 28, 10, 77, 25, 507, 206), |
| (66.4, 135, 0, 11.7, 62, 61, 595, 986, 22, 46, 77, 27, 529, 190), |
| (79.8, 152, 1, 8.7, 57, 53, 530, 986, 30, 72, 92, 43, 405, 264), |
| (94.6, 142, 1, 8.8, 81, 77, 497, 956, 33, 321, 116, 47, 427, 247), |
| (53.9, 143, 0, 11.0, 66, 63, 537, 977, 10, 6, 114, 35, 487, 166), |
| (92.9, 135, 1, 10.4, 123, 115, 537, 978, 31, 170, 89, 34, 631, 165), |
| (75.0, 130, 0, 11.6, 128, 128, 536, 934, 51, 24, 78, 34, 627, 135), |
| (122.5, 125, 0, 10.8, 113, 105, 567, 985, 78, 94, 130, 58, 626, 166), |
| (74.2, 126, 0, 10.8, 74, 67, 602, 984, 34, 12, 102, 33, 557, 195), |
| (43.9, 157, 1, 8.9, 47, 44, 512, 962, 22, 423, 97, 34, 288, 276), |
| (121.6, 132, 0, 9.6, 87, 83, 564, 953, 43, 92, 83, 32, 513, 227), |
| (96.8, 131, 0, 11.6, 78, 73, 574, 1038, 7, 36, 142, 42, 540, 176), |
| (52.3, 130, 0, 11.6, 63, 57, 641, 984, 14, 26, 70, 21, 486, 196), |
| (199.3, 131, 0, 12.1, 160, 143, 631, 1071, 3, 77, 102, 41, 674, 152), |
| (34.2, 135, 0, 10.9, 69, 71, 540, 965, 6, 4, 80, 22, 564, 139), |
| (121.6, 152, 0, 11.2, 82, 76, 571, 1018, 10, 79, 103, 28, 537, 215), |
| (104.3, 119, 0, 10.7, 166, 157, 521, 938, 168, 89, 92, 36, 637, 154), |
| (69.6, 166, 1, 8.9, 58, 54, 521, 973, 46, 254, 72, 26, 396, 237), |
| (37.3, 140, 0, 9.3, 55, 54, 535, 1045, 6, 20, 135, 40, 453, 200), |
| (75.4, 125, 0, 10.9, 90, 81, 586, 964, 97, 82, 105, 43, 617, 163), |
| (107.2, 147, 1, 10.4, 63, 64, 560, 972, 23, 95, 76, 24, 462, 233), |
| (92.3, 126, 0, 11.8, 97, 97, 542, 990, 18, 21, 102, 35, 589, 166), |
| (65.3, 123, 0, 10.2, 97, 87, 526, 948, 113, 76, 124, 50, 572, 158), |
| (127.2, 150, 0, 10.0, 109, 98, 531, 964, 9, 24, 87, 38, 559, 153), |
| (83.1, 177, 1, 8.7, 58, 56, 638, 974, 24, 349, 76, 28, 382, 254), |
| (56.6, 133, 0, 10.4, 51, 47, 599, 1024, 7, 40, 99, 27, 425, 225), |
| (82.6, 149, 1, 8.8, 61, 54, 515, 953, 36, 165, 86, 35, 395, 251), |
| (115.1, 145, 1, 10.4, 82, 74, 560, 981, 96, 126, 88, 31, 488, 228), |
| (88.0, 148, 0, 12.2, 72, 66, 601, 998, 9, 19, 84, 20, 590, 144), |
| (54.2, 141, 0, 10.9, 56, 54, 523, 968, 4, 2, 107, 37, 489, 170), |
| (82.3, 162, 1, 9.9, 75, 70, 522, 996, 40, 208, 73, 27, 496, 224), |
| (103.0, 136, 0, 12.1, 95, 96, 574, 1012, 29, 36, 111, 37, 622, 162), |
| (45.5, 139, 1, 8.8, 46, 41, 480, 968, 19, 49, 135, 53, 457, 249), |
| (50.8, 126, 0, 10.4, 106, 97, 599, 989, 40, 24, 78, 25, 593, 171), |
| (84.9, 130, 0, 12.1, 90, 91, 623, 1049, 3, 22, 113, 40, 588, 160); |
| |
| drop table if exists temp_result; |
| drop table if exists temp_result_summary; |
| select logregr_train( |
| 'crime', |
| 'temp_result', |
| 'crimerat >= 110', |
| 'array[1, maleteen, south, educ, police59]' |
| ); |
| |
| |
| SELECT assert( |
| relative_error(coef, ARRAY[-17.70177, .0833837, -1.117091, .0229224, .0581834]) < 1e-5 AND |
| relative_error(log_likelihood, -18.606959) < 1e-5 AND |
| relative_error(std_err, ARRAY[9.495993, .0440353, 1.359616, .5594047, .0210049]) < 1e-5 AND |
| relative_error(z_stats, ARRAY[-1.864, 1.894, -0.822, 0.041, 2.770]) < 1e-3 AND |
| relative_error(p_values, ARRAY[0.062, 0.058, 0.411, 0.967, 0.006]) < 1e-3 AND |
| relative_error(odds_ratios, ARRAY[exp(-17.70177), 1.086959, .3272305, 1.023187, 1.059909]) < 1e-5 AND |
| relative_error(condition_no, sqrt(14513863)) < 1e-2, |
| 'Logistic regression with IRLS optimizer (crime): Wrong results' |
| ) FROM temp_result; |
| |
| -- Neither the conjugate-gradient nor incremental-gradient-descent optimizers |
| -- perform reasonably here, so we do not test them. |
| |
| |
| /* |
| * The following example is taken from: |
| * http://www.ats.ucla.edu/stat/r/dae/logit.htm |
| * Predicting heart attack |
| */ |
| |
| DROP TABLE IF EXISTS grad_school; |
| DROP TABLE IF EXISTS grad_school_summary; |
| CREATE TABLE grad_school ( |
| id SERIAL NOT NULL, |
| admit INTEGER, |
| gre INTEGER, |
| gpa DOUBLE PRECISION, |
| rank INTEGER |
| )m4_ifdef(`__POSTGRESQL__', `', `DISTRIBUTED BY (id)'); |
| |
| COPY grad_school (admit, gre, gpa, rank) FROM STDIN; |
| 0 380 3.61 3 |
| 1 660 3.67 3 |
| 1 800 4 1 |
| 1 640 3.19 4 |
| 0 520 2.93 4 |
| 1 760 3 2 |
| 1 560 2.98 1 |
| 0 400 3.08 2 |
| 1 540 3.39 3 |
| 0 700 3.92 2 |
| 0 800 4 4 |
| 0 440 3.22 1 |
| 1 760 4 1 |
| 0 700 3.08 2 |
| 1 700 4 1 |
| 0 480 3.44 3 |
| 0 780 3.87 4 |
| 0 360 2.56 3 |
| 0 800 3.75 2 |
| 1 540 3.81 1 |
| 0 500 3.17 3 |
| 1 660 3.63 2 |
| 0 600 2.82 4 |
| 0 680 3.19 4 |
| 1 760 3.35 2 |
| 1 800 3.66 1 |
| 1 620 3.61 1 |
| 1 520 3.74 4 |
| 1 780 3.22 2 |
| 0 520 3.29 1 |
| 0 540 3.78 4 |
| 0 760 3.35 3 |
| 0 600 3.4 3 |
| 1 800 4 3 |
| 0 360 3.14 1 |
| 0 400 3.05 2 |
| 0 580 3.25 1 |
| 0 520 2.9 3 |
| 1 500 3.13 2 |
| 1 520 2.68 3 |
| 0 560 2.42 2 |
| 1 580 3.32 2 |
| 1 600 3.15 2 |
| 0 500 3.31 3 |
| 0 700 2.94 2 |
| 1 460 3.45 3 |
| 1 580 3.46 2 |
| 0 500 2.97 4 |
| 0 440 2.48 4 |
| 0 400 3.35 3 |
| 0 640 3.86 3 |
| 0 440 3.13 4 |
| 0 740 3.37 4 |
| 1 680 3.27 2 |
| 0 660 3.34 3 |
| 1 740 4 3 |
| 0 560 3.19 3 |
| 0 380 2.94 3 |
| 0 400 3.65 2 |
| 0 600 2.82 4 |
| 1 620 3.18 2 |
| 0 560 3.32 4 |
| 0 640 3.67 3 |
| 1 680 3.85 3 |
| 0 580 4 3 |
| 0 600 3.59 2 |
| 0 740 3.62 4 |
| 0 620 3.3 1 |
| 0 580 3.69 1 |
| 0 800 3.73 1 |
| 0 640 4 3 |
| 0 300 2.92 4 |
| 0 480 3.39 4 |
| 0 580 4 2 |
| 0 720 3.45 4 |
| 0 720 4 3 |
| 0 560 3.36 3 |
| 1 800 4 3 |
| 0 540 3.12 1 |
| 1 620 4 1 |
| 0 700 2.9 4 |
| 0 620 3.07 2 |
| 0 500 2.71 2 |
| 0 380 2.91 4 |
| 1 500 3.6 3 |
| 0 520 2.98 2 |
| 0 600 3.32 2 |
| 0 600 3.48 2 |
| 0 700 3.28 1 |
| 1 660 4 2 |
| 0 700 3.83 2 |
| 1 720 3.64 1 |
| 0 800 3.9 2 |
| 0 580 2.93 2 |
| 1 660 3.44 2 |
| 0 660 3.33 2 |
| 0 640 3.52 4 |
| 0 480 3.57 2 |
| 0 700 2.88 2 |
| 0 400 3.31 3 |
| 0 340 3.15 3 |
| 0 580 3.57 3 |
| 0 380 3.33 4 |
| 0 540 3.94 3 |
| 1 660 3.95 2 |
| 1 740 2.97 2 |
| 1 700 3.56 1 |
| 0 480 3.13 2 |
| 0 400 2.93 3 |
| 0 480 3.45 2 |
| 0 680 3.08 4 |
| 0 420 3.41 4 |
| 0 360 3 3 |
| 0 600 3.22 1 |
| 0 720 3.84 3 |
| 0 620 3.99 3 |
| 1 440 3.45 2 |
| 0 700 3.72 2 |
| 1 800 3.7 1 |
| 0 340 2.92 3 |
| 1 520 3.74 2 |
| 1 480 2.67 2 |
| 0 520 2.85 3 |
| 0 500 2.98 3 |
| 0 720 3.88 3 |
| 0 540 3.38 4 |
| 1 600 3.54 1 |
| 0 740 3.74 4 |
| 0 540 3.19 2 |
| 0 460 3.15 4 |
| 1 620 3.17 2 |
| 0 640 2.79 2 |
| 0 580 3.4 2 |
| 0 500 3.08 3 |
| 0 560 2.95 2 |
| 0 500 3.57 3 |
| 0 560 3.33 4 |
| 0 700 4 3 |
| 0 620 3.4 2 |
| 1 600 3.58 1 |
| 0 640 3.93 2 |
| 1 700 3.52 4 |
| 0 620 3.94 4 |
| 0 580 3.4 3 |
| 0 580 3.4 4 |
| 0 380 3.43 3 |
| 0 480 3.4 2 |
| 0 560 2.71 3 |
| 1 480 2.91 1 |
| 0 740 3.31 1 |
| 1 800 3.74 1 |
| 0 400 3.38 2 |
| 1 640 3.94 2 |
| 0 580 3.46 3 |
| 0 620 3.69 3 |
| 1 580 2.86 4 |
| 0 560 2.52 2 |
| 1 480 3.58 1 |
| 0 660 3.49 2 |
| 0 700 3.82 3 |
| 0 600 3.13 2 |
| 0 640 3.5 2 |
| 1 700 3.56 2 |
| 0 520 2.73 2 |
| 0 580 3.3 2 |
| 0 700 4 1 |
| 0 440 3.24 4 |
| 0 720 3.77 3 |
| 0 500 4 3 |
| 0 600 3.62 3 |
| 0 400 3.51 3 |
| 0 540 2.81 3 |
| 0 680 3.48 3 |
| 1 800 3.43 2 |
| 0 500 3.53 4 |
| 1 620 3.37 2 |
| 0 520 2.62 2 |
| 1 620 3.23 3 |
| 0 620 3.33 3 |
| 0 300 3.01 3 |
| 0 620 3.78 3 |
| 0 500 3.88 4 |
| 0 700 4 2 |
| 1 540 3.84 2 |
| 0 500 2.79 4 |
| 0 800 3.6 2 |
| 0 560 3.61 3 |
| 0 580 2.88 2 |
| 0 560 3.07 2 |
| 0 500 3.35 2 |
| 1 640 2.94 2 |
| 0 800 3.54 3 |
| 0 640 3.76 3 |
| 0 380 3.59 4 |
| 1 600 3.47 2 |
| 0 560 3.59 2 |
| 0 660 3.07 3 |
| 1 400 3.23 4 |
| 0 600 3.63 3 |
| 0 580 3.77 4 |
| 0 800 3.31 3 |
| 1 580 3.2 2 |
| 1 700 4 1 |
| 0 420 3.92 4 |
| 1 600 3.89 1 |
| 1 780 3.8 3 |
| 0 740 3.54 1 |
| 1 640 3.63 1 |
| 0 540 3.16 3 |
| 0 580 3.5 2 |
| 0 740 3.34 4 |
| 0 580 3.02 2 |
| 0 460 2.87 2 |
| 0 640 3.38 3 |
| 1 600 3.56 2 |
| 1 660 2.91 3 |
| 0 340 2.9 1 |
| 1 460 3.64 1 |
| 0 460 2.98 1 |
| 1 560 3.59 2 |
| 0 540 3.28 3 |
| 0 680 3.99 3 |
| 1 480 3.02 1 |
| 0 800 3.47 3 |
| 0 800 2.9 2 |
| 1 720 3.5 3 |
| 0 620 3.58 2 |
| 0 540 3.02 4 |
| 0 480 3.43 2 |
| 1 720 3.42 2 |
| 0 580 3.29 4 |
| 0 600 3.28 3 |
| 0 380 3.38 2 |
| 0 420 2.67 3 |
| 1 800 3.53 1 |
| 0 620 3.05 2 |
| 1 660 3.49 2 |
| 0 480 4 2 |
| 0 500 2.86 4 |
| 0 700 3.45 3 |
| 0 440 2.76 2 |
| 1 520 3.81 1 |
| 1 680 2.96 3 |
| 0 620 3.22 2 |
| 0 540 3.04 1 |
| 0 800 3.91 3 |
| 0 680 3.34 2 |
| 0 440 3.17 2 |
| 0 680 3.64 3 |
| 0 640 3.73 3 |
| 0 660 3.31 4 |
| 0 620 3.21 4 |
| 1 520 4 2 |
| 1 540 3.55 4 |
| 1 740 3.52 4 |
| 0 640 3.35 3 |
| 1 520 3.3 2 |
| 1 620 3.95 3 |
| 0 520 3.51 2 |
| 0 640 3.81 2 |
| 0 680 3.11 2 |
| 0 440 3.15 2 |
| 1 520 3.19 3 |
| 1 620 3.95 3 |
| 1 520 3.9 3 |
| 0 380 3.34 3 |
| 0 560 3.24 4 |
| 1 600 3.64 3 |
| 1 680 3.46 2 |
| 0 500 2.81 3 |
| 1 640 3.95 2 |
| 0 540 3.33 3 |
| 1 680 3.67 2 |
| 0 660 3.32 1 |
| 0 520 3.12 2 |
| 1 600 2.98 2 |
| 0 460 3.77 3 |
| 1 580 3.58 1 |
| 1 680 3 4 |
| 1 660 3.14 2 |
| 0 660 3.94 2 |
| 0 360 3.27 3 |
| 0 660 3.45 4 |
| 0 520 3.1 4 |
| 1 440 3.39 2 |
| 0 600 3.31 4 |
| 1 800 3.22 1 |
| 1 660 3.7 4 |
| 0 800 3.15 4 |
| 0 420 2.26 4 |
| 1 620 3.45 2 |
| 0 800 2.78 2 |
| 0 680 3.7 2 |
| 0 800 3.97 1 |
| 0 480 2.55 1 |
| 0 520 3.25 3 |
| 0 560 3.16 1 |
| 0 460 3.07 2 |
| 0 540 3.5 2 |
| 0 720 3.4 3 |
| 0 640 3.3 2 |
| 1 660 3.6 3 |
| 1 400 3.15 2 |
| 1 680 3.98 2 |
| 0 220 2.83 3 |
| 0 580 3.46 4 |
| 1 540 3.17 1 |
| 0 580 3.51 2 |
| 0 540 3.13 2 |
| 0 440 2.98 3 |
| 0 560 4 3 |
| 0 660 3.67 2 |
| 0 660 3.77 3 |
| 1 520 3.65 4 |
| 0 540 3.46 4 |
| 1 300 2.84 2 |
| 1 340 3 2 |
| 1 780 3.63 4 |
| 1 480 3.71 4 |
| 0 540 3.28 1 |
| 0 460 3.14 3 |
| 0 460 3.58 2 |
| 0 500 3.01 4 |
| 0 420 2.69 2 |
| 0 520 2.7 3 |
| 0 680 3.9 1 |
| 0 680 3.31 2 |
| 1 560 3.48 2 |
| 0 580 3.34 2 |
| 0 500 2.93 4 |
| 0 740 4 3 |
| 0 660 3.59 3 |
| 0 420 2.96 1 |
| 0 560 3.43 3 |
| 1 460 3.64 3 |
| 1 620 3.71 1 |
| 0 520 3.15 3 |
| 0 620 3.09 4 |
| 0 540 3.2 1 |
| 1 660 3.47 3 |
| 0 500 3.23 4 |
| 1 560 2.65 3 |
| 0 500 3.95 4 |
| 0 580 3.06 2 |
| 0 520 3.35 3 |
| 0 500 3.03 3 |
| 0 600 3.35 2 |
| 0 580 3.8 2 |
| 0 400 3.36 2 |
| 0 620 2.85 2 |
| 1 780 4 2 |
| 0 620 3.43 3 |
| 1 580 3.12 3 |
| 0 700 3.52 2 |
| 1 540 3.78 2 |
| 1 760 2.81 1 |
| 0 700 3.27 2 |
| 0 720 3.31 1 |
| 1 560 3.69 3 |
| 0 720 3.94 3 |
| 1 520 4 1 |
| 1 540 3.49 1 |
| 0 680 3.14 2 |
| 0 460 3.44 2 |
| 1 560 3.36 1 |
| 0 480 2.78 3 |
| 0 460 2.93 3 |
| 0 620 3.63 3 |
| 0 580 4 1 |
| 0 800 3.89 2 |
| 1 540 3.77 2 |
| 1 680 3.76 3 |
| 1 680 2.42 1 |
| 1 620 3.37 1 |
| 0 560 3.78 2 |
| 0 560 3.49 4 |
| 0 620 3.63 2 |
| 1 800 4 2 |
| 0 640 3.12 3 |
| 0 540 2.7 2 |
| 0 700 3.65 2 |
| 1 540 3.49 2 |
| 0 540 3.51 2 |
| 0 660 4 1 |
| 1 480 2.62 2 |
| 0 420 3.02 1 |
| 1 740 3.86 2 |
| 0 580 3.36 2 |
| 0 640 3.17 2 |
| 0 640 3.51 2 |
| 1 800 3.05 2 |
| 1 660 3.88 2 |
| 1 600 3.38 3 |
| 1 620 3.75 2 |
| 1 460 3.99 3 |
| 0 620 4 2 |
| 0 560 3.04 3 |
| 0 460 2.63 2 |
| 0 700 3.65 2 |
| 0 600 3.89 3 |
| \. |
| |
| drop table if exists temp_result; |
| drop table if exists temp_result_summary; |
| select logregr_train( |
| 'grad_school', |
| 'temp_result', |
| 'admit', |
| 'ARRAY[1, gre, gpa, (rank = 2)::INT::FLOAT8, (rank = 3)::INT::FLOAT8, (rank = 4)::INT::FLOAT8]' |
| ); |
| |
| SELECT assert( |
| relative_error(coef, ARRAY[-3.989979, 0.002264, 0.804038, -0.675443, -1.340204, -1.551464]) < 1e-5 AND |
| relative_error(log_likelihood, -229.2587) < 1e-5 AND |
| relative_error(std_err, ARRAY[1.139951, 0.001094, 0.331819, 0.316490, 0.345306, 0.417832]) < 1e-5 AND |
| relative_error(z_stats, ARRAY[-3.500, 2.070, 2.423, -2.134, -3.881, -3.713]) < 1e-3 AND |
| relative_error(p_values, ARRAY[0.000465, 0.038465, 0.015388, 0.032829, 0.000104, 0.000205]) < 1e-3 AND |
| relative_error(odds_ratios, ARRAY[0.0185001, 1.0022670, 2.2345448, 0.5089310, 0.2617923, 0.2119375]) < 1e-5 AND |
| relative_error(condition_no, 6379.028) < 1e-2, |
| 'Logistic regression with IRLS optimizer (grad_school): Wrong results' |
| ) FROM temp_result; |
| |
| select logregr_predict( |
| coef, |
| ARRAY[1, gre, gpa, (rank = 2)::INT::FLOAT8, (rank = 3)::INT::FLOAT8, (rank = 4)::INT::FLOAT8]) |
| FROM temp_result, grad_school; |
| |
| select logregr_predict_prob( |
| coef, |
| ARRAY[1, gre, gpa, (rank = 2)::INT::FLOAT8, (rank = 3)::INT::FLOAT8, (rank = 4)::INT::FLOAT8]) |
| FROM temp_result, grad_school; |
| |
| drop table if exists temp_result; |
| drop table if exists temp_result_summary; |
| select logregr_train( |
| 'grad_school', |
| 'temp_result', |
| 'admit', |
| 'ARRAY[1, gre, gpa, (rank = 2)::INT::FLOAT8, (rank = 3)::INT::FLOAT8, (rank = 4)::INT::FLOAT8]', |
| Null, |
| 20, |
| 'cg', |
| 0 |
| ); |
| |
| --- insert some NULL values for null handling testing |
| INSERT INTO grad_school (admit, gre, gpa, rank) VALUES |
| (NULL, NULL, 3, 4), |
| (1, NULL, 3, 5); |
| |
| drop table if exists temp_result; |
| drop table if exists temp_result_summary; |
| select logregr_train( |
| 'grad_school', |
| 'temp_result', |
| 'admit', |
| 'ARRAY[1, gre, gpa]', |
| 'rank'); |
| |
| |
| -- Even though we were far more generous for the conjugate-gradient optimizer, |
| -- but it still generates failures intermittently. We comment it out here |
| -- to acknowledge CG may generate un-converged results. |
| -- SELECT |
| -- assert(relative_error(coef, ARRAY[-3.989979, 0.002264, 0.804038, -0.675443, -1.340204, -1.551464]) < 0.1, 'Logistic regression with CG optimizer (grad_school): Wrong coef'), |
| -- assert(relative_error(log_likelihood, -229.2587) < 0.1, 'Logistic regression with CG optimizer (grad_school): Wrong log_likelihood'), |
| -- assert(relative_error(std_err, ARRAY[1.139951, 0.001094, 0.331819, 0.316490, 0.345306, 0.417832]) < 0.1, 'Logistic regression with CG optimizer (grad_school): Wrong std_err'), |
| -- assert(relative_error(z_stats, ARRAY[-3.500, 2.070, 2.423, -2.134, -3.881, -3.713]) < 0.1, 'Logistic regression with CG optimizer (grad_school): Wrong z-stats'), |
| -- assert(relative_error(p_values, ARRAY[0.000465, 0.038465, 0.015388, 0.032829, 0.000104, 0.000205]) < 1, 'Logistic regression with CG optimizer (grad_school): Wrong p-values'), |
| -- assert(relative_error(odds_ratios, ARRAY[0.0185001, 1.0022670, 2.2345448, 0.5089310, 0.2617923, 0.2119375]) < 0.1, 'Logistic regression with CG optimizer (grad_school): Wrong odds_ratios') |
| -- FROM temp_result; |
| |
| -- IGD essentially does not work for this case, so we are not testing it |
