Skip to content

Commit

Permalink
Merge pull request #5736 from jbrockmendel/reg_results
Browse files Browse the repository at this point in the history 
CLN: strict linting for regression/tests/results
  • Loading branch information
@bashtage
bashtage committed May 17, 2019
2 parents b50cc9d + 549e552 commit 6a735b7
Show file tree
Hide file tree
Showing 9 changed files with 2,440 additions and 2,208 deletions.
3 changes: 1 addition & 2 deletions lint.sh
View file
Expand Up @@ -40,8 +40,7 @@ if [ "$LINT" == true ]; then
statsmodels/regression/tests/test_lme.py \
statsmodels/regression/tests/test_processreg.py \
statsmodels/regression/tests/test_quantile_regression.py \
statsmodels/regression/tests/results/generate_lasso.py \
statsmodels/regression/tests/results/generate_lme.py \
statsmodels/regression/tests/results/ \
statsmodels/robust/tests/ \
statsmodels/sandbox/distributions/try_pot.py \
statsmodels/sandbox/panel/correlation_structures.py \
Expand Down
257 changes: 176 additions & 81 deletions statsmodels/regression/tests/results/glmnet_r_results.py
View file
@@ -1,163 +1,258 @@
import numpy as np

rslt_0 = np.array([100,2,0,38.51677,-0.0002828592,-0.01440669])
rslt_0 = np.array([
100, 2, 0, 38.51677, -0.0002828592, -0.01440669])

rslt_1 = np.array([100,2,0,5.991962,-0.00144282,-0.08156386])
rslt_1 = np.array([
100, 2, 0, 5.991962, -0.00144282, -0.08156386])

rslt_2 = np.array([100,2,0,0.9321552,-0.003400212,-0.2968083])
rslt_2 = np.array([
100, 2, 0, 0.9321552, -0.003400212, -0.2968083])

rslt_3 = np.array([100,2,0.5,0.2352493,0,-0.4099712])
rslt_3 = np.array([
100, 2, 0.5, 0.2352493, 0, -0.4099712])

rslt_4 = np.array([100,2,0.5,0.07703354,0,-0.5185569])
rslt_4 = np.array([
100, 2, 0.5, 0.07703354, 0, -0.5185569])

rslt_5 = np.array([100,2,0.5,0.02522501,0,-0.5578565])
rslt_5 = np.array([
100, 2, 0.5, 0.02522501, 0, -0.5578565])

rslt_6 = np.array([100,2,1,0.1416793,0,-0.4346288])
rslt_6 = np.array([
100, 2, 1, 0.1416793, 0, -0.4346288])

rslt_7 = np.array([100,2,1,0.04639359,0,-0.530877])
rslt_7 = np.array([
100, 2, 1, 0.04639359, 0, -0.530877])

rslt_8 = np.array([100,2,1,0.01829859,0,-0.5592558])
rslt_8 = np.array([
100, 2, 1, 0.01829859, 0, -0.5592558])

rslt_9 = np.array([100,3,0,38.51677,-0.0002931891,-0.01441805,0.01019003])
rslt_9 = np.array([
100, 3, 0, 38.51677, -0.0002931891, -0.01441805, 0.01019003])

rslt_10 = np.array([100,3,0,5.991962,-0.00177573,-0.08193033,0.05812813])
rslt_10 = np.array([
100, 3, 0, 5.991962, -0.00177573, -0.08193033, 0.05812813])

rslt_11 = np.array([100,3,0,0.9321552,-0.007886374,-0.3017527,0.2167204])
rslt_11 = np.array([
100, 3, 0, 0.9321552, -0.007886374, -0.3017527, 0.2167204])

rslt_12 = np.array([100,3,0.5,0.2143504,0,-0.4350311,0.2878706])
rslt_12 = np.array([
100, 3, 0.5, 0.2143504, 0, -0.4350311, 0.2878706])

rslt_13 = np.array([100,3,0.5,0.07019009,0,-0.540186,0.3830582])
rslt_13 = np.array([
100, 3, 0.5, 0.07019009, 0, -0.540186, 0.3830582])

rslt_14 = np.array([100,3,0.5,0.02298409,-0.007207869,-0.5779581,0.417658])
rslt_14 = np.array([
100, 3, 0.5, 0.02298409, -0.007207869, -0.5779581, 0.417658])

rslt_15 = np.array([100,3,1,0.1176247,0,-0.4728138,0.3103276])
rslt_15 = np.array([
100, 3, 1, 0.1176247, 0, -0.4728138, 0.3103276])

rslt_16 = np.array([100,3,1,0.03851677,0,-0.5564642,0.393978])
rslt_16 = np.array([
100, 3, 1, 0.03851677, 0, -0.5564642, 0.393978])

rslt_17 = np.array([100,3,1,0.01384221,-0.004948496,-0.5824816,0.4202673])
rslt_17 = np.array([
100, 3, 1, 0.01384221, -0.004948496, -0.5824816, 0.4202673])

rslt_18 = np.array([100,5,0,38.51677,-0.0002957436,-0.01439344,0.01022347,-0.001361073,0.01156754])
rslt_18 = np.array([
100, 5, 0, 38.51677, -0.0002957436,
-0.01439344, 0.01022347, -0.001361073, 0.01156754])

rslt_19 = np.array([100,5,0,5.991962,-0.001844459,-0.08115691,0.05920275,-0.006436703,0.06552873])
rslt_19 = np.array([
100, 5, 0, 5.991962, -0.001844459,
-0.08115691, 0.05920275, -0.006436703, 0.06552873])

rslt_20 = np.array([100,5,0,0.9321552,-0.008226096,-0.2921193,0.2311589,-0.008479325,0.2404427])
rslt_20 = np.array([
100, 5, 0, 0.9321552, -0.008226096,
-0.2921193, 0.2311589, -0.008479325, 0.2404427])

rslt_21 = np.array([100,5,0.5,0.2143504,0,-0.4132961,0.3222149,0,0.3262246])
rslt_21 = np.array([
100, 5, 0.5, 0.2143504, 0, -0.4132961, 0.3222149, 0, 0.3262246])

rslt_22 = np.array([100,5,0.5,0.0639546,0,-0.5145012,0.4359585,0,0.4294001])
rslt_22 = np.array([
100, 5, 0.5, 0.0639546, 0, -0.5145012, 0.4359585, 0, 0.4294001])

rslt_23 = np.array([100,5,0.5,0.02094225,-0.00787628,-0.5466386,0.4728884,0.0106367,0.4627422])
rslt_23 = np.array([
100, 5, 0.5, 0.02094225, -0.00787628,
-0.5466386, 0.4728884, 0.0106367, 0.4627422])

rslt_24 = np.array([100,5,1,0.1176247,0,-0.4469856,0.3513112,0,0.3519996])
rslt_24 = np.array([
100, 5, 1, 0.1176247, 0, -0.4469856, 0.3513112, 0, 0.3519996])

rslt_25 = np.array([100,5,1,0.03851677,0,-0.5243974,0.4448608,0,0.4370223])
rslt_25 = np.array([
100, 5, 1, 0.03851677, 0, -0.5243974, 0.4448608, 0, 0.4370223])

rslt_26 = np.array([100,5,1,0.01261251,-0.00600524,-0.5501652,0.4761259,0.009292623,0.4655236])
rslt_26 = np.array([
100, 5, 1, 0.01261251, -0.00600524,
-0.5501652, 0.4761259, 0.009292623, 0.4655236])

rslt_27 = np.array([200,2,0,37.37243,-0.0006197982,-0.01442844])
rslt_27 = np.array([
200, 2, 0, 37.37243, -0.0006197982, -0.01442844])

rslt_28 = np.array([200,2,0,5.813939,-0.002965157,-0.0813082])
rslt_28 = np.array([
200, 2, 0, 5.813939, -0.002965157, -0.0813082])

rslt_29 = np.array([200,2,0,0.9044607,-0.0046755,-0.2916905])
rslt_29 = np.array([
200, 2, 0, 0.9044607, -0.0046755, -0.2916905])

rslt_30 = np.array([200,2,0.5,0.207982,0,-0.4102587])
rslt_30 = np.array([
200, 2, 0.5, 0.207982, 0, -0.4102587])

rslt_31 = np.array([200,2,0.5,0.06810473,0,-0.5061677])
rslt_31 = np.array([
200, 2, 0.5, 0.06810473, 0, -0.5061677])

rslt_32 = np.array([200,2,0.5,0.02230123,0,-0.5404787])
rslt_32 = np.array([
200, 2, 0.5, 0.02230123, 0, -0.5404787])

rslt_33 = np.array([200,2,1,0.11413,0,-0.443054])
rslt_33 = np.array([
200, 2, 1, 0.11413, 0, -0.443054])

rslt_34 = np.array([200,2,1,0.03737243,0,-0.5201972])
rslt_34 = np.array([
200, 2, 1, 0.03737243, 0, -0.5201972])

rslt_35 = np.array([200,2,1,0.01343095,0,-0.544259])
rslt_35 = np.array([
200, 2, 1, 0.01343095, 0, -0.544259])

rslt_36 = np.array([200,3,0,37.37243,-0.0006182692,-0.01444479,0.01107838])
rslt_36 = np.array([
200, 3, 0, 37.37243, -0.0006182692, -0.01444479, 0.01107838])

rslt_37 = np.array([200,3,0,5.813939,-0.002912734,-0.08183274,0.06300276])
rslt_37 = np.array([
200, 3, 0, 5.813939, -0.002912734, -0.08183274, 0.06300276])

rslt_38 = np.array([200,3,0,0.9044607,-0.003838731,-0.298655,0.2325274])
rslt_38 = np.array([
200, 3, 0, 0.9044607, -0.003838731, -0.298655, 0.2325274])

rslt_39 = np.array([200,3,0.5,0.207982,0,-0.426489,0.3142956])
rslt_39 = np.array([
200, 3, 0.5, 0.207982, 0, -0.426489, 0.3142956])

rslt_40 = np.array([200,3,0.5,0.06810473,0,-0.5287444,0.4093069])
rslt_40 = np.array([
200, 3, 0.5, 0.06810473, 0, -0.5287444, 0.4093069])

rslt_41 = np.array([200,3,0.5,0.02230123,0,-0.5654985,0.4434813])
rslt_41 = np.array([
200, 3, 0.5, 0.02230123, 0, -0.5654985, 0.4434813])

rslt_42 = np.array([200,3,1,0.1252575,0,-0.450539,0.3272248])
rslt_42 = np.array([
200, 3, 1, 0.1252575, 0, -0.450539, 0.3272248])

rslt_43 = np.array([200,3,1,0.04101619,0,-0.540326,0.4170119])
rslt_43 = np.array([
200, 3, 1, 0.04101619, 0, -0.540326, 0.4170119])

rslt_44 = np.array([200,3,1,0.01343095,0,-0.5697273,0.4464131])
rslt_44 = np.array([
200, 3, 1, 0.01343095, 0, -0.5697273, 0.4464131])

rslt_45 = np.array([200,5,0,37.37243,-0.0006213086,-0.01441334,0.01110832,-0.00159415,0.01237059])
rslt_45 = np.array([
200, 5, 0, 37.37243, -0.0006213086,
-0.01441334, 0.01110832, -0.00159415, 0.01237059])

rslt_46 = np.array([200,5,0,5.813939,-0.003007826,-0.08084357,0.06395108,-0.008286656,0.06957285])
rslt_46 = np.array([
200, 5, 0, 5.813939, -0.003007826,
-0.08084357, 0.06395108, -0.008286656, 0.06957285])

rslt_47 = np.array([200,5,0,0.9044607,-0.004970054,-0.2862855,0.2446989,-0.02121838,0.2488685])
rslt_47 = np.array([
200, 5, 0, 0.9044607, -0.004970054,
-0.2862855, 0.2446989, -0.02121838, 0.2488685])

rslt_48 = np.array([200,5,0.5,0.207982,0,-0.398901,0.3436805,0,0.3354236])
rslt_48 = np.array([
200, 5, 0.5, 0.207982, 0, -0.398901, 0.3436805, 0, 0.3354236])

rslt_49 = np.array([200,5,0.5,0.06810473,0,-0.4912754,0.4492343,0,0.4280285])
rslt_49 = np.array([
200, 5, 0.5, 0.06810473, 0, -0.4912754, 0.4492343, 0, 0.4280285])

rslt_50 = np.array([200,5,0.5,0.02230123,0,-0.5240797,0.4869532,-0.007872715,0.4611676])
rslt_50 = np.array([
200, 5, 0.5, 0.02230123, 0,
-0.5240797, 0.4869532, -0.007872715, 0.4611676])

rslt_51 = np.array([200,5,1,0.11413,0,-0.4298238,0.3738058,0,0.3604706])
rslt_51 = np.array([
200, 5, 1, 0.11413, 0, -0.4298238, 0.3738058, 0, 0.3604706])

rslt_52 = np.array([200,5,1,0.03737243,0,-0.5044899,0.4632316,0,0.4395307])
rslt_52 = np.array([
200, 5, 1, 0.03737243, 0, -0.5044899, 0.4632316, 0, 0.4395307])

rslt_53 = np.array([200,5,1,0.01343095,0,-0.5276608,0.4907892,-0.005343287,0.4641237])
rslt_53 = np.array([
200, 5, 1, 0.01343095, 0, -0.5276608, 0.4907892, -0.005343287, 0.4641237])

rslt_54 = np.array([300,2,0,34.28346,-0.0003952878,-0.01440603])
rslt_54 = np.array([
300, 2, 0, 34.28346, -0.0003952878, -0.01440603])

rslt_55 = np.array([300,2,0,5.333396,-0.001501322,-0.08029644])
rslt_55 = np.array([
300, 2, 0, 5.333396, -0.001501322, -0.08029644])

rslt_56 = np.array([300,2,0,0.8297037,0.002123792,-0.2786158])
rslt_56 = np.array([
300, 2, 0, 0.8297037, 0.002123792, -0.2786158])

rslt_57 = np.array([300,2,0.5,0.1907915,0,-0.3787717])
rslt_57 = np.array([
300, 2, 0.5, 0.1907915, 0, -0.3787717])

rslt_58 = np.array([300,2,0.5,0.06247562,0,-0.4648646])
rslt_58 = np.array([
300, 2, 0.5, 0.06247562, 0, -0.4648646])

rslt_59 = np.array([300,2,0.5,0.02045795,0.008282189,-0.4960003])
rslt_59 = np.array([
300, 2, 0.5, 0.02045795, 0.008282189, -0.4960003])

rslt_60 = np.array([300,2,1,0.1046967,0,-0.4057543])
rslt_60 = np.array([
300, 2, 1, 0.1046967, 0, -0.4057543])

rslt_61 = np.array([300,2,1,0.03428346,0,-0.476403])
rslt_61 = np.array([
300, 2, 1, 0.03428346, 0, -0.476403])

rslt_62 = np.array([300,2,1,0.01232084,0.006463487,-0.4988773])
rslt_62 = np.array([
300, 2, 1, 0.01232084, 0.006463487, -0.4988773])

rslt_63 = np.array([300,3,0,34.28346,-0.000389794,-0.01441521,0.01276503])
rslt_63 = np.array([
300, 3, 0, 34.28346, -0.000389794, -0.01441521, 0.01276503])

rslt_64 = np.array([300,3,0,5.333396,-0.001327446,-0.08058424,0.07142295])
rslt_64 = np.array([
300, 3, 0, 5.333396, -0.001327446, -0.08058424, 0.07142295])

rslt_65 = np.array([300,3,0,0.8297037,0.004333533,-0.2821765,0.2505531])
rslt_65 = np.array([
300, 3, 0, 0.8297037, 0.004333533, -0.2821765, 0.2505531])

rslt_66 = np.array([300,3,0.5,0.2093935,0,-0.3745606,0.3227598])
rslt_66 = np.array([
300, 3, 0.5, 0.2093935, 0, -0.3745606, 0.3227598])

rslt_67 = np.array([300,3,0.5,0.06856692,0,-0.4707627,0.4155033])
rslt_67 = np.array([
300, 3, 0.5, 0.06856692, 0, -0.4707627, 0.4155033])

rslt_68 = np.array([300,3,0.5,0.02045795,0.01586083,-0.5078635,0.4505092])
rslt_68 = np.array([
300, 3, 0.5, 0.02045795, 0.01586083, -0.5078635, 0.4505092])

rslt_69 = np.array([300,3,1,0.1149045,0,-0.4043541,0.347238])
rslt_69 = np.array([
300, 3, 1, 0.1149045, 0, -0.4043541, 0.347238])

rslt_70 = np.array([300,3,1,0.03762605,0,-0.483917,0.4268009])
rslt_70 = np.array([
300, 3, 1, 0.03762605, 0, -0.483917, 0.4268009])

rslt_71 = np.array([300,3,1,0.01232084,0.01417268,-0.5109371,0.4530943])
rslt_71 = np.array([
300, 3, 1, 0.01232084, 0.01417268, -0.5109371, 0.4530943])

rslt_72 = np.array([300,5,0,34.28346,-0.0003887545,-0.01441065,0.01279253,-0.001336729,0.01332977])
rslt_72 = np.array([
300, 5, 0, 34.28346, -0.0003887545,
-0.01441065, 0.01279253, -0.001336729, 0.01332977])

rslt_73 = np.array([300,5,0,5.333396,-0.001294265,-0.08044256,0.07228947,-0.006888477,0.07488032])
rslt_73 = np.array([
300, 5, 0, 5.333396, -0.001294265,
-0.08044256, 0.07228947, -0.006888477, 0.07488032])

rslt_74 = np.array([300,5,0,0.8297037,0.00476926,-0.2804704,0.2614478,-0.01761104,0.2661808])
rslt_74 = np.array([
300, 5, 0, 0.8297037, 0.00476926,
-0.2804704, 0.2614478, -0.01761104, 0.2661808])

rslt_75 = np.array([300,5,0.5,0.2093935,0,-0.3705814,0.347887,0,0.351998])
rslt_75 = np.array([
300, 5, 0.5, 0.2093935, 0, -0.3705814, 0.347887, 0, 0.351998])

rslt_76 = np.array([300,5,0.5,0.06856692,0,-0.465371,0.449883,0,0.4508697])
rslt_76 = np.array([
300, 5, 0.5, 0.06856692, 0, -0.465371, 0.449883, 0, 0.4508697])

rslt_77 = np.array([300,5,0.5,0.02045795,0.01744528,-0.5021737,0.4881574,-0.007930221,0.4878982])
rslt_77 = np.array([
300, 5, 0.5, 0.02045795, 0.01744528,
-0.5021737, 0.4881574, -0.007930221, 0.4878982])

rslt_78 = np.array([300,5,1,0.1149045,0,-0.3996701,0.3772643,0,0.3807041])
rslt_78 = np.array([
300, 5, 1, 0.1149045, 0, -0.3996701, 0.3772643, 0, 0.3807041])

rslt_79 = np.array([300,5,1,0.03762605,0,-0.4782089,0.4633918,0,0.4639368])
rslt_79 = np.array([
300, 5, 1, 0.03762605, 0, -0.4782089, 0.4633918, 0, 0.4639368])

rslt_80 = np.array([300,5,1,0.01232084,0.01578941,-0.5051176,0.4915133,-0.005703398,0.4910846])
rslt_80 = np.array([
300, 5, 1, 0.01232084, 0.01578941,
-0.5051176, 0.4915133, -0.005703398, 0.4910846])

0 comments on commit 6a735b7

Please sign in to comment.