diff --git a/h2o-py/tests/testdir_algos/glm/pyunit_GH_6722_beta_equality_loose_lessthan_linear_constraints_binomial.py b/h2o-py/tests/testdir_algos/glm/pyunit_GH_6722_beta_equality_loose_lessthan_linear_constraints_binomial.py index de25483eabc6..2946a34247f4 100644 --- a/h2o-py/tests/testdir_algos/glm/pyunit_GH_6722_beta_equality_loose_lessthan_linear_constraints_binomial.py +++ b/h2o-py/tests/testdir_algos/glm/pyunit_GH_6722_beta_equality_loose_lessthan_linear_constraints_binomial.py @@ -161,7 +161,7 @@ def test_constraints_binomial(): constraint_alpha = [0.01] constraint_beta = [0.5, 0.9] constraint_c0 = [40] - h2o_glm_random_init = utils_for_glm_tests.constraint_glm_gridsearch(train, predictors, response, solver="IRLSM", + h2o_glm_random_init = utils_for_glm_hglm_tests.constraint_glm_gridsearch(train, predictors, response, solver="IRLSM", family="binomial", linear_constraints=linear_constraints2, beta_constraints=beta_constraints, @@ -174,12 +174,12 @@ def test_constraints_binomial(): return_best=False) init_random_logloss = h2o_glm_random_init.model_performance()._metric_json['logloss'] print("logloss with constraints and coefficients initialized random initial values: {0}, number of iterations" - " taken to build the model: {1}".format(init_random_logloss, utils_for_glm_tests.find_model_iterations(h2o_glm_random_init))) + " taken to build the model: {1}".format(init_random_logloss, utils_for_glm_hglm_tests.find_model_iterations(h2o_glm_random_init))) print(glm.getConstraintsInfo(h2o_glm_random_init)) # GLM model with GLM coefficients with default initialization - h2o_glm_default_init = utils_for_glm_tests.constraint_glm_gridsearch(train, predictors, response, solver="IRLSM", + h2o_glm_default_init = utils_for_glm_hglm_tests.constraint_glm_gridsearch(train, predictors, response, solver="IRLSM", family="binomial", linear_constraints=linear_constraints2, beta_constraints=beta_constraints, @@ -191,7 +191,7 @@ def test_constraints_binomial(): return_best=False) default_init_logloss = h2o_glm_default_init.model_performance()._metric_json['logloss'] print("logloss with constraints and default coefficients initialization: {0}, number of iterations" - " taken to build the model: {1}".format(default_init_logloss, utils_for_glm_tests.find_model_iterations(h2o_glm_default_init))) + " taken to build the model: {1}".format(default_init_logloss, utils_for_glm_hglm_tests.find_model_iterations(h2o_glm_default_init))) print(glm.getConstraintsInfo(h2o_glm_default_init)) diff --git a/h2o-py/tests/testdir_algos/glm/pyunit_GH_6722_beta_linear_constraints_binomial_objective_likelihood.py b/h2o-py/tests/testdir_algos/glm/pyunit_GH_6722_beta_linear_constraints_binomial_objective_likelihood.py index 482d5333bef0..a29114bd44e6 100644 --- a/h2o-py/tests/testdir_algos/glm/pyunit_GH_6722_beta_linear_constraints_binomial_objective_likelihood.py +++ b/h2o-py/tests/testdir_algos/glm/pyunit_GH_6722_beta_linear_constraints_binomial_objective_likelihood.py @@ -94,7 +94,7 @@ def test_constraints_objective_likelihood(): obj_optimal = h2o_glm_optimal_init.average_objective() print("logloss with constraints and coefficients initialized with glm model built without constraints: {0}, aic: " "{2}, llh: {3}, average_objective: {4}, number of iterations taken to build the model: " - "{1}".format(init_logloss, utils_for_glm_tests.find_model_iterations(h2o_glm_optimal_init), aic_optimal, + "{1}".format(init_logloss, utils_for_glm_hglm_tests.find_model_iterations(h2o_glm_optimal_init), aic_optimal, ll_optimal, obj_optimal)) print(glm.getConstraintsInfo(h2o_glm_optimal_init)) @@ -124,7 +124,7 @@ def test_constraints_objective_likelihood(): init_random_logloss = h2o_glm_random_init.model_performance()._metric_json['logloss'] print("logloss with constraints and coefficients initialized random initial values: {0}, aic: {2}, llh: {3}, " "average objective: {4}, number of iterations taken to build the model: " - "{1}".format(init_random_logloss, utils_for_glm_tests.find_model_iterations(h2o_glm_random_init), aic_random, + "{1}".format(init_random_logloss, utils_for_glm_hglm_tests.find_model_iterations(h2o_glm_random_init), aic_random, ll_random, obj_random)) print(glm.getConstraintsInfo(h2o_glm_random_init)) @@ -141,7 +141,7 @@ def test_constraints_objective_likelihood(): default_init_logloss = h2o_glm_default_init.model_performance()._metric_json['logloss'] print("logloss with constraints and default coefficients initialization: {0}, aic: {2}, llh: {3}, average objective:" " {4}, number of iterations taken to build the model: " - "{1}".format(default_init_logloss, utils_for_glm_tests.find_model_iterations(h2o_glm_default_init), aic_default, + "{1}".format(default_init_logloss, utils_for_glm_hglm_tests.find_model_iterations(h2o_glm_default_init), aic_default, ll_default, obj_default)) print(glm.getConstraintsInfo(h2o_glm_default_init)) diff --git a/h2o-py/tests/testdir_algos/glm/pyunit_GH_6722_equality_constraints_only_binomial.py b/h2o-py/tests/testdir_algos/glm/pyunit_GH_6722_equality_constraints_only_binomial.py index b47615522018..f3399f798f95 100644 --- a/h2o-py/tests/testdir_algos/glm/pyunit_GH_6722_equality_constraints_only_binomial.py +++ b/h2o-py/tests/testdir_algos/glm/pyunit_GH_6722_equality_constraints_only_binomial.py @@ -94,7 +94,7 @@ def test_equality_constraints_only_binomial(): constraint_alpha = [0.01] constraint_beta = [0.1] constraint_c0 = [15, 20] - h2o_glm_random_init = utils_for_glm_tests.constraint_glm_gridsearch(train, predictors, response, solver="IRLSM", + h2o_glm_random_init = utils_for_glm_hglm_tests.constraint_glm_gridsearch(train, predictors, response, solver="IRLSM", family="binomial", linear_constraints=linear_constraints2, startval=random_coef, @@ -110,7 +110,7 @@ def test_equality_constraints_only_binomial(): print(glm.getConstraintsInfo(h2o_glm_random_init)) # GLM model with GLM coefficients with default initialization - h2o_glm_default_init = utils_for_glm_tests.constraint_glm_gridsearch(train, predictors, response, solver="IRLSM", + h2o_glm_default_init = utils_for_glm_hglm_tests.constraint_glm_gridsearch(train, predictors, response, solver="IRLSM", family="binomial", linear_constraints=linear_constraints2, constraint_eta0=constraint_eta0, diff --git a/h2o-py/tests/testdir_algos/glm/pyunit_GH_6722_equality_loose_lessthan_linear_constraints_binomial.py b/h2o-py/tests/testdir_algos/glm/pyunit_GH_6722_equality_loose_lessthan_linear_constraints_binomial.py index 6b26d80d8eff..62980c816cc0 100644 --- a/h2o-py/tests/testdir_algos/glm/pyunit_GH_6722_equality_loose_lessthan_linear_constraints_binomial.py +++ b/h2o-py/tests/testdir_algos/glm/pyunit_GH_6722_equality_loose_lessthan_linear_constraints_binomial.py @@ -128,7 +128,7 @@ def test_equality_linear_constraints_binomial(): constraint_alpha = [0.1] constraint_beta = [0.9] constraint_c0 = [10] # initial value - h2o_glm_random_init = utils_for_glm_tests.constraint_glm_gridsearch(train, predictors, response, solver="IRLSM", + h2o_glm_random_init = utils_for_glm_hglm_tests.constraint_glm_gridsearch(train, predictors, response, solver="IRLSM", family="binomial", linear_constraints=linear_constraints2, startval=random_coef, @@ -139,11 +139,11 @@ def test_equality_linear_constraints_binomial(): constraint_c0=constraint_c0, return_best=False) init_random_logloss = h2o_glm_random_init.model_performance()._metric_json['logloss'] print("logloss with constraints and coefficients initialized random initial values: {0}, number of iterations" - " taken to build the model: {1}".format(init_random_logloss, utils_for_glm_tests.find_model_iterations(h2o_glm_random_init))) + " taken to build the model: {1}".format(init_random_logloss, utils_for_glm_hglm_tests.find_model_iterations(h2o_glm_random_init))) print(glm.getConstraintsInfo(h2o_glm_random_init)) # GLM model with GLM coefficients with default initialization - h2o_glm_default_init = utils_for_glm_tests.constraint_glm_gridsearch(train, predictors, response, solver="IRLSM", + h2o_glm_default_init = utils_for_glm_hglm_tests.constraint_glm_gridsearch(train, predictors, response, solver="IRLSM", family="binomial", linear_constraints=linear_constraints2, constraint_eta0=constraint_eta0, @@ -153,7 +153,7 @@ def test_equality_linear_constraints_binomial(): constraint_c0=constraint_c0, return_best=False) default_init_logloss = h2o_glm_default_init.model_performance()._metric_json['logloss'] print("logloss with constraints and default coefficients initialization: {0}, number of iterations" - " taken to build the model: {1}".format(default_init_logloss, utils_for_glm_tests.find_model_iterations(h2o_glm_default_init))) + " taken to build the model: {1}".format(default_init_logloss, utils_for_glm_hglm_tests.find_model_iterations(h2o_glm_default_init))) print(glm.getConstraintsInfo(h2o_glm_default_init)) assert abs(logloss-init_logloss)<1e-6, "logloss from optimal GLM {0} and logloss from GLM with loose constraints " \ diff --git a/h2o-py/tests/testdir_algos/glm/pyunit_GH_6722_light_tight_beta_equality_lessthan_constraints_binomial.py b/h2o-py/tests/testdir_algos/glm/pyunit_GH_6722_light_tight_beta_equality_lessthan_constraints_binomial.py index 603fa082982c..e9048d9e2d43 100644 --- a/h2o-py/tests/testdir_algos/glm/pyunit_GH_6722_light_tight_beta_equality_lessthan_constraints_binomial.py +++ b/h2o-py/tests/testdir_algos/glm/pyunit_GH_6722_light_tight_beta_equality_lessthan_constraints_binomial.py @@ -132,7 +132,7 @@ def test_light_tight_linear_constraints_binomial(): constraint_beta = [0.9] constraint_c0 = [5, 10] # initial value # GLM model with with GLM coefficients set to GLM model coefficients built without constraints - h2o_glm_optimal_init = utils_for_glm_tests.constraint_glm_gridsearch(train, predictors, response, solver="IRLSM", + h2o_glm_optimal_init = utils_for_glm_hglm_tests.constraint_glm_gridsearch(train, predictors, response, solver="IRLSM", family="binomial", linear_constraints=linear_constraints2, beta_constraints=beta_constraints, @@ -145,10 +145,10 @@ def test_light_tight_linear_constraints_binomial(): return_best=False, epsilon=0.5) optimal_init_logloss = h2o_glm_optimal_init.model_performance()._metric_json['logloss'] print("logloss with optimal GLM coefficient initializaiton: {0}, number of iterations taken to build the model: " - "{1}".format(optimal_init_logloss, utils_for_glm_tests.find_model_iterations(h2o_glm_optimal_init))) + "{1}".format(optimal_init_logloss, utils_for_glm_hglm_tests.find_model_iterations(h2o_glm_optimal_init))) print(glm.getConstraintsInfo(h2o_glm_optimal_init)) - h2o_glm_default_init = utils_for_glm_tests.constraint_glm_gridsearch(train, predictors, response, solver="IRLSM", + h2o_glm_default_init = utils_for_glm_hglm_tests.constraint_glm_gridsearch(train, predictors, response, solver="IRLSM", family="binomial", linear_constraints=linear_constraints2, beta_constraints=beta_constraints, @@ -161,7 +161,7 @@ def test_light_tight_linear_constraints_binomial(): return_best=False, epsilon=0.5) default_init_logloss = h2o_glm_default_init.model_performance()._metric_json['logloss'] print("logloss with default GLM coefficient initializaiton: {0}, number of iterations taken to build the model: " - "{1}".format(default_init_logloss, utils_for_glm_tests.find_model_iterations(h2o_glm_default_init))) + "{1}".format(default_init_logloss, utils_for_glm_hglm_tests.find_model_iterations(h2o_glm_default_init))) print(glm.getConstraintsInfo(h2o_glm_default_init)) random_coef = [0.9740393731418461, 0.9021970400494406, 0.8337282995102272, 0.20588758679724872, 0.12522385214612453, 0.6390730524643073, 0.7055779213989253, 0.9004255614099713, 0.4075431157767999, 0.161093231584713, @@ -178,7 +178,7 @@ def test_light_tight_linear_constraints_binomial(): 0.4941250734508458, 0.5446841276322587, 0.19222703209695946, 0.9232239752817498, 0.8824688635063289, 0.224690851359456, 0.5809304720756304, 0.36863807988348585] - h2o_glm_random_init = utils_for_glm_tests.constraint_glm_gridsearch(train, predictors, response, solver="IRLSM", + h2o_glm_random_init = utils_for_glm_hglm_tests.constraint_glm_gridsearch(train, predictors, response, solver="IRLSM", family="binomial", linear_constraints=linear_constraints2, beta_constraints=beta_constraints, @@ -192,7 +192,7 @@ def test_light_tight_linear_constraints_binomial(): return_best=False, epsilon=0.5) random_init_logloss = h2o_glm_random_init.model_performance()._metric_json['logloss'] print("logloss with random GLM coefficient initializaiton: {0}, number of iterations taken to build the model: " - "{1}".format(random_init_logloss, utils_for_glm_tests.find_model_iterations(h2o_glm_random_init))) + "{1}".format(random_init_logloss, utils_for_glm_hglm_tests.find_model_iterations(h2o_glm_random_init))) print(glm.getConstraintsInfo(h2o_glm_random_init)) assert logloss <= optimal_init_logloss, "logloss from optimal GLM {0} should be lower than logloss from GLM with light tight" \ diff --git a/h2o-py/tests/testdir_algos/glm/pyunit_GH_6722_light_tight_equality_lessthan_constraints_binomial.py b/h2o-py/tests/testdir_algos/glm/pyunit_GH_6722_light_tight_equality_lessthan_constraints_binomial.py index c79708e299a0..f5c7050d3c56 100644 --- a/h2o-py/tests/testdir_algos/glm/pyunit_GH_6722_light_tight_equality_lessthan_constraints_binomial.py +++ b/h2o-py/tests/testdir_algos/glm/pyunit_GH_6722_light_tight_equality_lessthan_constraints_binomial.py @@ -111,7 +111,7 @@ def test_light_tight_linear_constraints_only_binomial(): constraint_beta = [0.9] constraint_c0 = [10, 20] # initial value # GLM model with with GLM coefficients set to GLM model coefficients built without constraints - h2o_glm_optimal_init = utils_for_glm_tests.constraint_glm_gridsearch(train, predictors, response, solver="IRLSM", + h2o_glm_optimal_init = utils_for_glm_hglm_tests.constraint_glm_gridsearch(train, predictors, response, solver="IRLSM", family="binomial", linear_constraints=linear_constraints2, init_optimal_glm=True, @@ -123,10 +123,10 @@ def test_light_tight_linear_constraints_only_binomial(): return_best=False) optimal_init_logloss = h2o_glm_optimal_init.model_performance()._metric_json['logloss'] print("logloss with optimal GLM coefficient initializaiton: {0}, number of iterations taken to build the model: " - "{1}".format(optimal_init_logloss, utils_for_glm_tests.find_model_iterations(h2o_glm_optimal_init))) + "{1}".format(optimal_init_logloss, utils_for_glm_hglm_tests.find_model_iterations(h2o_glm_optimal_init))) print(glm.getConstraintsInfo(h2o_glm_optimal_init)) - h2o_glm_default_init = utils_for_glm_tests.constraint_glm_gridsearch(train, predictors, response, solver="IRLSM", + h2o_glm_default_init = utils_for_glm_hglm_tests.constraint_glm_gridsearch(train, predictors, response, solver="IRLSM", family="binomial", linear_constraints=linear_constraints2, init_optimal_glm=False, @@ -139,7 +139,7 @@ def test_light_tight_linear_constraints_only_binomial(): epsilon=5e-1) default_init_logloss = h2o_glm_default_init.model_performance()._metric_json['logloss'] print("logloss with default GLM coefficient initializaiton: {0}, number of iterations taken to build the model: " - "{1}".format(default_init_logloss, utils_for_glm_tests.find_model_iterations(h2o_glm_default_init))) + "{1}".format(default_init_logloss, utils_for_glm_hglm_tests.find_model_iterations(h2o_glm_default_init))) print(glm.getConstraintsInfo(h2o_glm_default_init)) random_coef = [0.9740393731418461, 0.9021970400494406, 0.8337282995102272, 0.20588758679724872, 0.12522385214612453, 0.6390730524643073, 0.7055779213989253, 0.9004255614099713, 0.4075431157767999, 0.161093231584713, @@ -156,7 +156,7 @@ def test_light_tight_linear_constraints_only_binomial(): 0.4941250734508458, 0.5446841276322587, 0.19222703209695946, 0.9232239752817498, 0.8824688635063289, 0.224690851359456, 0.5809304720756304, 0.36863807988348585] - h2o_glm_random_init = utils_for_glm_tests.constraint_glm_gridsearch(train, predictors, response, solver="IRLSM", + h2o_glm_random_init = utils_for_glm_hglm_tests.constraint_glm_gridsearch(train, predictors, response, solver="IRLSM", family="binomial", linear_constraints=linear_constraints2, startval=random_coef, @@ -170,7 +170,7 @@ def test_light_tight_linear_constraints_only_binomial(): epsilon=5e-1) random_init_logloss = h2o_glm_random_init.model_performance()._metric_json['logloss'] print("logloss with random GLM coefficient initializaiton: {0}, number of iterations taken to build the model: " - "{1}".format(random_init_logloss, utils_for_glm_tests.find_model_iterations(h2o_glm_random_init))) + "{1}".format(random_init_logloss, utils_for_glm_hglm_tests.find_model_iterations(h2o_glm_random_init))) print(glm.getConstraintsInfo(h2o_glm_random_init)) assert abs(logloss - optimal_init_logloss)<1e-6, "logloss from optimal GLM {0} should be close to logloss from GLM with light tight" \ diff --git a/h2o-py/tests/testdir_algos/glm/pyunit_GH_6722_light_tight_linear_constraints_only_binomial.py b/h2o-py/tests/testdir_algos/glm/pyunit_GH_6722_light_tight_linear_constraints_only_binomial.py index de4a8b9461ef..dca483a98834 100644 --- a/h2o-py/tests/testdir_algos/glm/pyunit_GH_6722_light_tight_linear_constraints_only_binomial.py +++ b/h2o-py/tests/testdir_algos/glm/pyunit_GH_6722_light_tight_linear_constraints_only_binomial.py @@ -125,7 +125,7 @@ def test_light_tight_linear_constraints_only_binomial(): constraint_beta = [0.9] constraint_c0 = [1.2, 5] # initial value # GLM model with with GLM coefficients set to GLM model coefficients built without constraints - h2o_glm_optimal_init = utils_for_glm_tests.constraint_glm_gridsearch(train, predictors, response, solver="IRLSM", + h2o_glm_optimal_init = utils_for_glm_hglm_tests.constraint_glm_gridsearch(train, predictors, response, solver="IRLSM", family="binomial", linear_constraints=linear_constraints2, init_optimal_glm=True, @@ -137,11 +137,11 @@ def test_light_tight_linear_constraints_only_binomial(): return_best=False) optimal_init_logloss = h2o_glm_optimal_init.model_performance()._metric_json['logloss'] print("logloss with optimal GLM coefficient initializaiton: {0}, number of iterations taken to build the model: " - "{1}".format(optimal_init_logloss, utils_for_glm_tests.find_model_iterations(h2o_glm_optimal_init))) + "{1}".format(optimal_init_logloss, utils_for_glm_hglm_tests.find_model_iterations(h2o_glm_optimal_init))) print(glm.getConstraintsInfo(h2o_glm_optimal_init)) print("All constraints satisfied: {0}".format(glm.allConstraintsPassed(h2o_glm_optimal_init))) - h2o_glm_default_init = utils_for_glm_tests.constraint_glm_gridsearch(train, predictors, response, solver="IRLSM", + h2o_glm_default_init = utils_for_glm_hglm_tests.constraint_glm_gridsearch(train, predictors, response, solver="IRLSM", family="binomial", linear_constraints=linear_constraints2, init_optimal_glm=False, @@ -153,7 +153,7 @@ def test_light_tight_linear_constraints_only_binomial(): return_best=False) default_init_logloss = h2o_glm_default_init.model_performance()._metric_json['logloss'] print("logloss with default GLM coefficient initializaiton: {0}, number of iterations taken to build the model: " - "{1}".format(default_init_logloss, utils_for_glm_tests.find_model_iterations(h2o_glm_default_init))) + "{1}".format(default_init_logloss, utils_for_glm_hglm_tests.find_model_iterations(h2o_glm_default_init))) print(glm.getConstraintsInfo(h2o_glm_default_init)) print("All constraints satisfied: {0}".format(glm.allConstraintsPassed(h2o_glm_default_init))) @@ -172,7 +172,7 @@ def test_light_tight_linear_constraints_only_binomial(): 0.4941250734508458, 0.5446841276322587, 0.19222703209695946, 0.9232239752817498, 0.8824688635063289, 0.224690851359456, 0.5809304720756304, 0.36863807988348585] - h2o_glm_random_init = utils_for_glm_tests.constraint_glm_gridsearch(train, predictors, response, solver="IRLSM", + h2o_glm_random_init = utils_for_glm_hglm_tests.constraint_glm_gridsearch(train, predictors, response, solver="IRLSM", family="binomial", linear_constraints=linear_constraints2, startval=random_coef, @@ -185,7 +185,7 @@ def test_light_tight_linear_constraints_only_binomial(): return_best=False) random_init_logloss = h2o_glm_random_init.model_performance()._metric_json['logloss'] print("logloss with random GLM coefficient initializaiton: {0}, number of iterations taken to build the model: " - "{1}".format(random_init_logloss, utils_for_glm_tests.find_model_iterations(h2o_glm_random_init))) + "{1}".format(random_init_logloss, utils_for_glm_hglm_tests.find_model_iterations(h2o_glm_random_init))) print(glm.getConstraintsInfo(h2o_glm_random_init)) print("All constraints satisfied: {0}".format(glm.allConstraintsPassed(h2o_glm_random_init))) diff --git a/h2o-py/tests/testdir_algos/glm/pyunit_GH_6722_loose_beta_linear_constraints_binomial.py b/h2o-py/tests/testdir_algos/glm/pyunit_GH_6722_loose_beta_linear_constraints_binomial.py index 9b24b01f2469..00178a814543 100644 --- a/h2o-py/tests/testdir_algos/glm/pyunit_GH_6722_loose_beta_linear_constraints_binomial.py +++ b/h2o-py/tests/testdir_algos/glm/pyunit_GH_6722_loose_beta_linear_constraints_binomial.py @@ -115,7 +115,7 @@ def test_loose_beta_linear_constraints_binomial(): constraint_beta = [0.5] constraint_c0 = [2] # initial value # GLM model with GLM coefficients with default initialization - h2o_glm_random_init = utils_for_glm_tests.constraint_glm_gridsearch(train, predictors, response, solver="IRLSM", + h2o_glm_random_init = utils_for_glm_hglm_tests.constraint_glm_gridsearch(train, predictors, response, solver="IRLSM", family="binomial", beta_constraints=beta_constraints, linear_constraints=linear_constraints2, @@ -127,11 +127,11 @@ def test_loose_beta_linear_constraints_binomial(): constraint_c0=constraint_c0) init_random_logloss = h2o_glm_random_init.model_performance()._metric_json['logloss'] print("logloss with constraints and coefficients initialized random initial values: {0}, number of iterations" - " taken to build the model: {1}".format(init_random_logloss, utils_for_glm_tests.find_model_iterations(h2o_glm_random_init))) + " taken to build the model: {1}".format(init_random_logloss, utils_for_glm_hglm_tests.find_model_iterations(h2o_glm_random_init))) print(glm.getConstraintsInfo(h2o_glm_random_init)) # GLM model with GLM coefficients with default initialization - h2o_glm_default_init = utils_for_glm_tests.constraint_glm_gridsearch(train, predictors, response, solver="IRLSM", + h2o_glm_default_init = utils_for_glm_hglm_tests.constraint_glm_gridsearch(train, predictors, response, solver="IRLSM", family="binomial", beta_constraints=beta_constraints, linear_constraints=linear_constraints2, @@ -142,7 +142,7 @@ def test_loose_beta_linear_constraints_binomial(): constraint_c0=constraint_c0) default_init_logloss = h2o_glm_default_init.model_performance()._metric_json['logloss'] print("logloss with constraints and default coefficients initialization: {0}, number of iterations" - " taken to build the model: {1}".format(default_init_logloss, utils_for_glm_tests.find_model_iterations(h2o_glm_default_init))) + " taken to build the model: {1}".format(default_init_logloss, utils_for_glm_hglm_tests.find_model_iterations(h2o_glm_default_init))) print(glm.getConstraintsInfo(h2o_glm_default_init)) diff --git a/h2o-py/tests/testdir_algos/glm/pyunit_GH_6722_loose_only_linear_constraints_binomial.py b/h2o-py/tests/testdir_algos/glm/pyunit_GH_6722_loose_only_linear_constraints_binomial.py index b4c868404f7c..d38fd22097f0 100644 --- a/h2o-py/tests/testdir_algos/glm/pyunit_GH_6722_loose_only_linear_constraints_binomial.py +++ b/h2o-py/tests/testdir_algos/glm/pyunit_GH_6722_loose_only_linear_constraints_binomial.py @@ -92,7 +92,7 @@ def test_loose_linear_constraints_binomial(): constraint_alpha = [0.01] constraint_beta = [0.5] constraint_c0 = [5, 10] # initial value - h2o_glm_random_init = utils_for_glm_tests.constraint_glm_gridsearch(train, predictors, response, solver="IRLSM", + h2o_glm_random_init = utils_for_glm_hglm_tests.constraint_glm_gridsearch(train, predictors, response, solver="IRLSM", family="binomial", linear_constraints=linear_constraints2, startval=random_coef, @@ -103,11 +103,11 @@ def test_loose_linear_constraints_binomial(): constraint_c0=constraint_c0) random_init_logloss = h2o_glm_random_init.model_performance()._metric_json['logloss'] print("logloss with random coefficient initializaiton: {0}, number of iterations taken to build the model: " - "{1}".format(random_init_logloss, utils_for_glm_tests.find_model_iterations(h2o_glm_random_init))) + "{1}".format(random_init_logloss, utils_for_glm_hglm_tests.find_model_iterations(h2o_glm_random_init))) print(glm.getConstraintsInfo(h2o_glm_random_init)) # GLM model with GLM coefficients with default initialization - h2o_glm_default_init = utils_for_glm_tests.constraint_glm_gridsearch(train, predictors, response, solver="IRLSM", + h2o_glm_default_init = utils_for_glm_hglm_tests.constraint_glm_gridsearch(train, predictors, response, solver="IRLSM", family="binomial", linear_constraints=linear_constraints2, constraint_eta0=constraint_eta0, @@ -117,7 +117,7 @@ def test_loose_linear_constraints_binomial(): constraint_c0=constraint_c0) default_init_logloss = h2o_glm_default_init.model_performance()._metric_json['logloss'] print("logloss with default coefficient initializaiton: {0}, number of iterations taken to build the model: " - "{1}".format(default_init_logloss, utils_for_glm_tests.find_model_iterations(h2o_glm_default_init))) + "{1}".format(default_init_logloss, utils_for_glm_hglm_tests.find_model_iterations(h2o_glm_default_init))) print(glm.getConstraintsInfo(h2o_glm_default_init)) # since the constraints are loose, performance of GLM model without linear constraints and GLM model with linear diff --git a/h2o-py/tests/testdir_algos/glm/pyunit_GH_6722_separate_linear_beta_gaussian.py b/h2o-py/tests/testdir_algos/glm/pyunit_GH_6722_separate_linear_beta_gaussian.py index e05b4b186647..084291387511 100644 --- a/h2o-py/tests/testdir_algos/glm/pyunit_GH_6722_separate_linear_beta_gaussian.py +++ b/h2o-py/tests/testdir_algos/glm/pyunit_GH_6722_separate_linear_beta_gaussian.py @@ -1,7 +1,6 @@ import h2o from h2o.estimators.glm import H2OGeneralizedLinearEstimator as glm from tests import pyunit_utils -from tests.pyunit_utils import utils_for_glm_hglm_tests def test_separate_linear_beta_gaussian(): ''' diff --git a/h2o-py/tests/testdir_algos/glm/pyunit_GH_6722_tight_beta_equality_linear_constraints_binomial.py b/h2o-py/tests/testdir_algos/glm/pyunit_GH_6722_tight_beta_equality_linear_constraints_binomial.py index 270e7d303ca6..5945f5606b94 100644 --- a/h2o-py/tests/testdir_algos/glm/pyunit_GH_6722_tight_beta_equality_linear_constraints_binomial.py +++ b/h2o-py/tests/testdir_algos/glm/pyunit_GH_6722_tight_beta_equality_linear_constraints_binomial.py @@ -185,7 +185,7 @@ def test_tight_beta_linear_constraints_binomial(): constraint_beta = [0.001, 0.5] constraint_c0 = [20, 30] # initial value - h2o_glm_default_init = utils_for_glm_tests.constraint_glm_gridsearch(train, predictors, response, solver="IRLSM", + h2o_glm_default_init = utils_for_glm_hglm_tests.constraint_glm_gridsearch(train, predictors, response, solver="IRLSM", family="binomial", linear_constraints=linear_constraints2, beta_constraints=beta_constraints, @@ -198,10 +198,10 @@ def test_tight_beta_linear_constraints_binomial(): return_best=False, epsilon=20) default_init_logloss = h2o_glm_default_init.model_performance()._metric_json['logloss'] print("logloss with default GLM coefficient initializaiton: {0}, number of iterations taken to build the model: " - "{1}".format(default_init_logloss, utils_for_glm_tests.find_model_iterations(h2o_glm_default_init))) + "{1}".format(default_init_logloss, utils_for_glm_hglm_tests.find_model_iterations(h2o_glm_default_init))) # GLM model with with GLM coefficients set to GLM model coefficients built without constraints - h2o_glm_optimal_init = utils_for_glm_tests.constraint_glm_gridsearch(train, predictors, response, solver="IRLSM", + h2o_glm_optimal_init = utils_for_glm_hglm_tests.constraint_glm_gridsearch(train, predictors, response, solver="IRLSM", family="binomial", linear_constraints=linear_constraints2, beta_constraints=beta_constraints, @@ -215,11 +215,11 @@ def test_tight_beta_linear_constraints_binomial(): epsilon=20) optimal_init_logloss = h2o_glm_optimal_init.model_performance()._metric_json['logloss'] print("logloss with optimal GLM coefficient initializaiton: {0}, number of iterations taken to build the model: " - "{1}".format(optimal_init_logloss, utils_for_glm_tests.find_model_iterations(h2o_glm_optimal_init))) + "{1}".format(optimal_init_logloss, utils_for_glm_hglm_tests.find_model_iterations(h2o_glm_optimal_init))) print(glm.getConstraintsInfo(h2o_glm_optimal_init)) print("All constraints satisfied: {0}".format(glm.allConstraintsPassed(h2o_glm_optimal_init))) - h2o_glm_default_init = utils_for_glm_tests.constraint_glm_gridsearch(train, predictors, response, solver="IRLSM", + h2o_glm_default_init = utils_for_glm_hglm_tests.constraint_glm_gridsearch(train, predictors, response, solver="IRLSM", family="binomial", linear_constraints=linear_constraints2, beta_constraints=beta_constraints, @@ -232,7 +232,7 @@ def test_tight_beta_linear_constraints_binomial(): return_best=False, epsilon=20) default_init_logloss = h2o_glm_default_init.model_performance()._metric_json['logloss'] print("logloss with default GLM coefficient initializaiton: {0}, number of iterations taken to build the model: " - "{1}".format(default_init_logloss, utils_for_glm_tests.find_model_iterations(h2o_glm_default_init))) + "{1}".format(default_init_logloss, utils_for_glm_hglm_tests.find_model_iterations(h2o_glm_default_init))) print(glm.getConstraintsInfo(h2o_glm_default_init)) print("All constraints satisfied: {0}".format(glm.allConstraintsPassed(h2o_glm_default_init))) @@ -251,7 +251,7 @@ def test_tight_beta_linear_constraints_binomial(): 0.4941250734508458, 0.5446841276322587, 0.19222703209695946, 0.9232239752817498, 0.8824688635063289, 0.224690851359456, 0.5809304720756304, 0.36863807988348585] - h2o_glm_random_init = utils_for_glm_tests.constraint_glm_gridsearch(train, predictors, response, solver="IRLSM", + h2o_glm_random_init = utils_for_glm_hglm_tests.constraint_glm_gridsearch(train, predictors, response, solver="IRLSM", family="binomial", linear_constraints=linear_constraints2, beta_constraints=beta_constraints, @@ -265,7 +265,7 @@ def test_tight_beta_linear_constraints_binomial(): return_best=False, epsilon=20) random_init_logloss = h2o_glm_random_init.model_performance()._metric_json['logloss'] print("logloss with random GLM coefficient initializaiton: {0}, number of iterations taken to build the model: " - "{1}".format(random_init_logloss, utils_for_glm_tests.find_model_iterations(h2o_glm_random_init))) + "{1}".format(random_init_logloss, utils_for_glm_hglm_tests.find_model_iterations(h2o_glm_random_init))) print(glm.getConstraintsInfo(h2o_glm_random_init)) print("All constraints satisfied: {0}".format(glm.allConstraintsPassed(h2o_glm_random_init))) diff --git a/h2o-py/tests/testdir_algos/glm/pyunit_GH_6722_tight_equality_linear_constraints_binomial.py b/h2o-py/tests/testdir_algos/glm/pyunit_GH_6722_tight_equality_linear_constraints_binomial.py index 6fd0737d4656..a87a8f891dbd 100644 --- a/h2o-py/tests/testdir_algos/glm/pyunit_GH_6722_tight_equality_linear_constraints_binomial.py +++ b/h2o-py/tests/testdir_algos/glm/pyunit_GH_6722_tight_equality_linear_constraints_binomial.py @@ -161,7 +161,7 @@ def test_tight_equality_linear_constraints_binomial(): constraint_beta = [0.001] constraint_c0 = [1.5, 5] # initial value # GLM model with with GLM coefficients set to GLM model coefficients built without constraints - h2o_glm_optimal_init = utils_for_glm_tests.constraint_glm_gridsearch(train, predictors, response, solver="IRLSM", + h2o_glm_optimal_init = utils_for_glm_hglm_tests.constraint_glm_gridsearch(train, predictors, response, solver="IRLSM", family="binomial", linear_constraints=linear_constraints2, init_optimal_glm=True, @@ -173,11 +173,11 @@ def test_tight_equality_linear_constraints_binomial(): return_best=False) optimal_init_logloss = h2o_glm_optimal_init.model_performance()._metric_json['logloss'] print("logloss with optimal GLM coefficient initializaiton: {0}, number of iterations taken to build the model: " - "{1}".format(optimal_init_logloss, utils_for_glm_tests.find_model_iterations(h2o_glm_optimal_init))) + "{1}".format(optimal_init_logloss, utils_for_glm_hglm_tests.find_model_iterations(h2o_glm_optimal_init))) print(glm.getConstraintsInfo(h2o_glm_optimal_init)) print("All constraints satisfied: {0}".format(glm.allConstraintsPassed(h2o_glm_optimal_init))) - h2o_glm_default_init = utils_for_glm_tests.constraint_glm_gridsearch(train, predictors, response, solver="IRLSM", + h2o_glm_default_init = utils_for_glm_hglm_tests.constraint_glm_gridsearch(train, predictors, response, solver="IRLSM", family="binomial", linear_constraints=linear_constraints2, init_optimal_glm=False, @@ -189,7 +189,7 @@ def test_tight_equality_linear_constraints_binomial(): return_best=False) default_init_logloss = h2o_glm_default_init.model_performance()._metric_json['logloss'] print("logloss with default GLM coefficient initializaiton: {0}, number of iterations taken to build the model: " - "{1}".format(default_init_logloss, utils_for_glm_tests.find_model_iterations(h2o_glm_default_init))) + "{1}".format(default_init_logloss, utils_for_glm_hglm_tests.find_model_iterations(h2o_glm_default_init))) print(glm.getConstraintsInfo(h2o_glm_default_init)) print("All constraints satisfied: {0}".format(glm.allConstraintsPassed(h2o_glm_default_init))) @@ -208,7 +208,7 @@ def test_tight_equality_linear_constraints_binomial(): 0.4941250734508458, 0.5446841276322587, 0.19222703209695946, 0.9232239752817498, 0.8824688635063289, 0.224690851359456, 0.5809304720756304, 0.36863807988348585] - h2o_glm_random_init = utils_for_glm_tests.constraint_glm_gridsearch(train, predictors, response, solver="IRLSM", + h2o_glm_random_init = utils_for_glm_hglm_tests.constraint_glm_gridsearch(train, predictors, response, solver="IRLSM", family="binomial", linear_constraints=linear_constraints2, startval=random_coef, @@ -221,7 +221,7 @@ def test_tight_equality_linear_constraints_binomial(): return_best=False) random_init_logloss = h2o_glm_random_init.model_performance()._metric_json['logloss'] print("logloss with random GLM coefficient initializaiton: {0}, number of iterations taken to build the model: " - "{1}".format(random_init_logloss, utils_for_glm_tests.find_model_iterations(h2o_glm_random_init))) + "{1}".format(random_init_logloss, utils_for_glm_hglm_tests.find_model_iterations(h2o_glm_random_init))) print(glm.getConstraintsInfo(h2o_glm_random_init)) print("All constraints satisfied: {0}".format(glm.allConstraintsPassed(h2o_glm_random_init))) diff --git a/h2o-py/tests/testdir_algos/glm/pyunit_GH_6722_tight_linear_constraints_only_binomial.py b/h2o-py/tests/testdir_algos/glm/pyunit_GH_6722_tight_linear_constraints_only_binomial.py index 9b6e9ecda316..f14b2f860907 100644 --- a/h2o-py/tests/testdir_algos/glm/pyunit_GH_6722_tight_linear_constraints_only_binomial.py +++ b/h2o-py/tests/testdir_algos/glm/pyunit_GH_6722_tight_linear_constraints_only_binomial.py @@ -125,7 +125,7 @@ def test_tight_linear_constraints_binomial(): constraint_beta = [0.9] constraint_c0 = [10, 12] # initial value # GLM model with with GLM coefficients set to GLM model coefficients built without constraints - h2o_glm_optimal_init = utils_for_glm_tests.constraint_glm_gridsearch(train, predictors, response, solver="IRLSM", + h2o_glm_optimal_init = utils_for_glm_hglm_tests.constraint_glm_gridsearch(train, predictors, response, solver="IRLSM", family="binomial", linear_constraints=linear_constraints2, init_optimal_glm=True, @@ -137,11 +137,11 @@ def test_tight_linear_constraints_binomial(): return_best=False) optimal_init_logloss = h2o_glm_optimal_init.model_performance()._metric_json['logloss'] print("logloss with optimal GLM coefficient initializaiton: {0}, number of iterations taken to build the model: " - "{1}".format(optimal_init_logloss, utils_for_glm_tests.find_model_iterations(h2o_glm_optimal_init))) + "{1}".format(optimal_init_logloss, utils_for_glm_hglm_tests.find_model_iterations(h2o_glm_optimal_init))) print(glm.getConstraintsInfo(h2o_glm_optimal_init)) print("All constraints satisfied: {0}".format(glm.allConstraintsPassed(h2o_glm_optimal_init))) - h2o_glm_default_init = utils_for_glm_tests.constraint_glm_gridsearch(train, predictors, response, solver="IRLSM", + h2o_glm_default_init = utils_for_glm_hglm_tests.constraint_glm_gridsearch(train, predictors, response, solver="IRLSM", family="binomial", linear_constraints=linear_constraints2, init_optimal_glm=False, @@ -153,7 +153,7 @@ def test_tight_linear_constraints_binomial(): return_best=False) default_init_logloss = h2o_glm_default_init.model_performance()._metric_json['logloss'] print("logloss with default GLM coefficient initializaiton: {0}, number of iterations taken to build the model: " - "{1}".format(default_init_logloss, utils_for_glm_tests.find_model_iterations(h2o_glm_default_init))) + "{1}".format(default_init_logloss, utils_for_glm_hglm_tests.find_model_iterations(h2o_glm_default_init))) print(glm.getConstraintsInfo(h2o_glm_default_init)) print("All constraints satisfied: {0}".format(glm.allConstraintsPassed(h2o_glm_default_init))) @@ -172,7 +172,7 @@ def test_tight_linear_constraints_binomial(): 0.4941250734508458, 0.5446841276322587, 0.19222703209695946, 0.9232239752817498, 0.8824688635063289, 0.224690851359456, 0.5809304720756304, 0.36863807988348585] - h2o_glm_random_init = utils_for_glm_tests.constraint_glm_gridsearch(train, predictors, response, solver="IRLSM", + h2o_glm_random_init = utils_for_glm_hglm_tests.constraint_glm_gridsearch(train, predictors, response, solver="IRLSM", family="binomial", linear_constraints=linear_constraints2, startval=random_coef, @@ -185,7 +185,7 @@ def test_tight_linear_constraints_binomial(): return_best=False) random_init_logloss = h2o_glm_random_init.model_performance()._metric_json['logloss'] print("logloss with random GLM coefficient initializaiton: {0}, number of iterations taken to build the model: " - "{1}".format(random_init_logloss, utils_for_glm_tests.find_model_iterations(h2o_glm_random_init))) + "{1}".format(random_init_logloss, utils_for_glm_hglm_tests.find_model_iterations(h2o_glm_random_init))) print(glm.getConstraintsInfo(h2o_glm_random_init)) print("All constraints satisfied: {0}".format(glm.allConstraintsPassed(h2o_glm_random_init)))