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GH-16312: fix wrong error raised by duplicated/conflicted constraints.
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wendycwong
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Jul 1, 2024
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144 changes: 144 additions & 0 deletions
144
h2o-py/tests/testdir_algos/glm/pyunit_GH_16312_contrained_GLM_bad_constraints_large.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,144 @@ | ||
| import h2o | ||
| from h2o.estimators.glm import H2OGeneralizedLinearEstimator as glm | ||
| from tests import pyunit_utils | ||
| import numpy as np | ||
| import pandas as pd | ||
|
|
||
| # no need to check anything, this test just needs to run into completion duplicating/conflicting constraints | ||
| def data_prep(seed): | ||
| np.random.seed(seed) | ||
| x1 = np.random.normal(0, 10, 100000) | ||
| x2 = np.random.normal(10, 100 , 100000) | ||
| x3 = np.random.normal(20, 200, 100000) | ||
| x4 = np.random.normal(30, 3000, 100000) | ||
| x5 = np.random.normal(400, 4000, 100000) | ||
|
|
||
| y_raw = np.sin(x1)*100 + np.sin(x2)*100 + x3/20 + x3/30 + x5/400 | ||
| y = np.random.normal(y_raw, 20) | ||
|
|
||
| data = { | ||
| 'x1': x1, | ||
| 'x2': x2, | ||
| 'x3': x3, | ||
| 'x4': x4, | ||
| 'x5': x5, | ||
| 'y': y, | ||
| } | ||
| return h2o.H2OFrame(pd.DataFrame(data)) | ||
|
|
||
| def test_bad_lambda_specification(): | ||
| train_data = data_prep(123) | ||
| family = 'gaussian' | ||
| link = 'identity' | ||
| nfolds = 0 | ||
| lambda_ = 0.0 | ||
| seed = 1234 | ||
| calc_like = True | ||
| compute_p_values = True | ||
| solver = 'irlsm' | ||
| predictors = ['x1', 'x2', 'x3', 'x4', 'x5'] | ||
| response = "y" | ||
|
|
||
| linear_constraints2 = [] | ||
|
|
||
| name = "x2" | ||
| values = 1 | ||
| types = "LessThanEqual" | ||
| contraint_numbers = 0 | ||
| linear_constraints2.append([name, values, types, contraint_numbers]) | ||
|
|
||
| name = "x3" | ||
| values = -1 | ||
| types = "LessThanEqual" | ||
| contraint_numbers = 0 | ||
| linear_constraints2.append([name, values, types, contraint_numbers]) | ||
|
|
||
| name = "constant" | ||
| values = 0 | ||
| types = "LessThanEqual" | ||
| contraint_numbers = 0 | ||
| linear_constraints2.append([name, values, types, contraint_numbers]) | ||
|
|
||
| name = "x3" | ||
| values = 1 | ||
| types = "LessThanEqual" | ||
| contraint_numbers = 1 | ||
| linear_constraints2.append([name, values, types, contraint_numbers]) | ||
|
|
||
| name = "x4" | ||
| values = -1 | ||
| types = "LessThanEqual" | ||
| contraint_numbers = 1 | ||
| linear_constraints2.append([name, values, types, contraint_numbers]) | ||
|
|
||
| name = "constant" | ||
| values = 0 | ||
| types = "LessThanEqual" | ||
| contraint_numbers = 1 | ||
| linear_constraints2.append([name, values, types, contraint_numbers]) | ||
|
|
||
| name = "x2" | ||
| values = 1 | ||
| types = "LessThanEqual" | ||
| contraint_numbers = 2 | ||
| linear_constraints2.append([name, values, types, contraint_numbers]) | ||
|
|
||
| name = "x3" | ||
| values = 1 | ||
| types = "LessThanEqual" | ||
| contraint_numbers = 2 | ||
| linear_constraints2.append([name, values, types, contraint_numbers]) | ||
|
|
||
| name = "x4" | ||
| values = 1 | ||
| types = "LessThanEqual" | ||
| contraint_numbers = 2 | ||
| linear_constraints2.append([name, values, types, contraint_numbers]) | ||
|
|
||
| name = "constant" | ||
| values = 0 | ||
| types = "LessThanEqual" | ||
| contraint_numbers = 2 | ||
| linear_constraints2.append([name, values, types, contraint_numbers]) | ||
|
|
||
| linear_constraints = h2o.H2OFrame(linear_constraints2) | ||
| linear_constraints.set_names(["names", "values", "types", "constraint_numbers"]) | ||
|
|
||
| params = { | ||
| "family" : family, | ||
| "link": link, | ||
| "lambda_" : lambda_, | ||
| "seed" : seed, | ||
| "nfolds" : nfolds, | ||
| "compute_p_values" : compute_p_values, | ||
| "calc_like" : calc_like, | ||
| "solver" : solver, | ||
| "linear_constraints": linear_constraints | ||
| } | ||
|
|
||
| model = glm(**params) | ||
| model.train(x = predictors, y = response, training_frame = train_data) | ||
| print(model.coef()) | ||
| coef_constrained = model.coef() | ||
| print(glm.getConstraintsInfo(model)) | ||
|
|
||
| params = { | ||
| "family" : family, | ||
| "link": link, | ||
| "lambda_" : lambda_, | ||
| "seed" : seed, | ||
| "nfolds" : nfolds, | ||
| "compute_p_values" : compute_p_values, | ||
| "calc_like" : calc_like, | ||
| "solver" : solver, | ||
| } | ||
|
|
||
| model_no_constraints = glm(**params) | ||
| model_no_constraints.train(x = predictors, y = response, training_frame = train_data) | ||
| coef_no_constraints = model_no_constraints.coef() | ||
| print(coef_no_constraints) | ||
|
|
||
| if __name__ == "__main__": | ||
| pyunit_utils.standalone_test(test_bad_lambda_specification) | ||
| else: | ||
| test_bad_lambda_specification() |
131 changes: 131 additions & 0 deletions
131
h2o-py/tests/testdir_algos/glm/pyunit_GH_16312_contrained_GLM_lambda_test_large.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,131 @@ | ||
| import h2o | ||
| from h2o.estimators.glm import H2OGeneralizedLinearEstimator as glm | ||
| from tests import pyunit_utils | ||
| import numpy as np | ||
| import pandas as pd | ||
|
|
||
| # no need to check anything, this test just needs to run into completion without NPE error. | ||
| def data_prep(seed): | ||
| np.random.seed(seed) | ||
| x1 = np.random.normal(0, 10, 100000) | ||
| x2 = np.random.normal(10, 100 , 100000) | ||
| x3 = np.random.normal(20, 200, 100000) | ||
| x4 = np.random.normal(30, 3000, 100000) | ||
| x5 = np.random.normal(400, 4000, 100000) | ||
|
|
||
| y_raw = np.sin(x1)*100 + np.sin(x2)*100 + x3/20 + x3/30 + x5/400 | ||
| y = np.random.normal(y_raw, 20) | ||
|
|
||
| data = { | ||
| 'x1': x1, | ||
| 'x2': x2, | ||
| 'x3': x3, | ||
| 'x4': x4, | ||
| 'x5': x5, | ||
| 'y': y, | ||
| } | ||
| return h2o.H2OFrame(pd.DataFrame(data)) | ||
|
|
||
| def test_bad_lambda_specification(): | ||
| train_data = data_prep(123) | ||
| family = 'gaussian' | ||
| link = 'identity' | ||
| nfolds = 0 | ||
| lambda_ = 0.0 | ||
| seed = 1234 | ||
| calc_like = True | ||
| compute_p_values = True | ||
| solver = 'irlsm' | ||
| predictors = ['x1', 'x2', 'x3', 'x4', 'x5'] | ||
| response = "y" | ||
|
|
||
| linear_constraints2 = [] | ||
|
|
||
| name = "x2" | ||
| values = 1 | ||
| types = "LessThanEqual" | ||
| contraint_numbers = 0 | ||
| linear_constraints2.append([name, values, types, contraint_numbers]) | ||
|
|
||
| name = "x3" | ||
| values = -1 | ||
| types = "LessThanEqual" | ||
| contraint_numbers = 0 | ||
| linear_constraints2.append([name, values, types, contraint_numbers]) | ||
|
|
||
| name = "constant" | ||
| values = 0 | ||
| types = "LessThanEqual" | ||
| contraint_numbers = 0 | ||
| linear_constraints2.append([name, values, types, contraint_numbers]) | ||
|
|
||
| name = "x3" | ||
| values = 1 | ||
| types = "LessThanEqual" | ||
| contraint_numbers = 1 | ||
| linear_constraints2.append([name, values, types, contraint_numbers]) | ||
|
|
||
| name = "x4" | ||
| values = -1 | ||
| types = "LessThanEqual" | ||
| contraint_numbers = 1 | ||
| linear_constraints2.append([name, values, types, contraint_numbers]) | ||
|
|
||
| name = "constant" | ||
| values = 0 | ||
| types = "LessThanEqual" | ||
| contraint_numbers = 1 | ||
| linear_constraints2.append([name, values, types, contraint_numbers]) | ||
|
|
||
| name = "x2" | ||
| values = 1 | ||
| types = "LessThanEqual" | ||
| contraint_numbers = 2 | ||
| linear_constraints2.append([name, values, types, contraint_numbers]) | ||
|
|
||
| name = "x3" | ||
| values = 1 | ||
| types = "LessThanEqual" | ||
| contraint_numbers = 2 | ||
| linear_constraints2.append([name, values, types, contraint_numbers]) | ||
|
|
||
| name = "x4" | ||
| values = 1 | ||
| types = "LessThanEqual" | ||
| contraint_numbers = 2 | ||
| linear_constraints2.append([name, values, types, contraint_numbers]) | ||
|
|
||
| name = "constant" | ||
| values = 0 | ||
| types = "LessThanEqual" | ||
| contraint_numbers = 2 | ||
| linear_constraints2.append([name, values, types, contraint_numbers]) | ||
|
|
||
|
|
||
| linear_constraints = h2o.H2OFrame(linear_constraints2) | ||
| linear_constraints.set_names(["names", "values", "types", "constraint_numbers"]) | ||
|
|
||
| linear_constraints = h2o.H2OFrame(linear_constraints2) | ||
| linear_constraints.set_names(["names", "values", "types", "constraint_numbers"]) | ||
| # check lower bound of beta constraint will not generate error but lambda will. | ||
| params = { | ||
| "family" : family, | ||
| "link": link, | ||
| "lambda_" : lambda_, | ||
| "seed" : seed, | ||
| "nfolds" : nfolds, | ||
| "compute_p_values" : compute_p_values, | ||
| "calc_like" : calc_like, | ||
| "solver" : solver, | ||
| "linear_constraints": linear_constraints | ||
| } | ||
|
|
||
| model = glm(**params) | ||
| model.train(x = predictors, y = response, training_frame = train_data) | ||
| print(model.coef()) | ||
|
|
||
|
|
||
| if __name__ == "__main__": | ||
| pyunit_utils.standalone_test(test_bad_lambda_specification) | ||
| else: | ||
| test_bad_lambda_specification() |
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