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20 hours ago •
IDAES
IDAES
Pysmo fails on ARM64
On both macOS and all Linux distress I tried with ARM64 processors Pysmo tests fails. I can't really see a clear reason by the test output is below.
=================================== FAILURES ===================================
______________ TestPysmoSurrogate.test_evaluate_unisurrogate_poly ______________
self = <idaes.core.surrogate.tests.test_pysmo_surrogate.TestPysmoSurrogate object at 0xffff71c686a0>
pysmo_surr1 = <idaes.core.surrogate.pysmo_surrogate.PysmoSurrogate object at 0xffff8abae430>
@pytest.mark.unit
def test_evaluate_unisurrogate_poly(self, pysmo_surr1):
# Test ``evaluate_surrogate`` for one output with interaction terms
x = [
-2,
-1.8,
-1.6,
-1.4,
-1.2,
-1.0,
-0.8,
-0.6,
-0.4,
-0.2,
0,
0.2,
0.4,
0.6,
0.8,
1.0,
1.2,
1.4,
1.6,
1.8,
2.0,
]
inputs = np.array([np.tile(x, len(x)), np.repeat(x, len(x))])
inputs = pd.DataFrame(inputs.transpose(), columns=["x1", "x2"])
out = pysmo_surr1.evaluate_surrogate(inputs)
for i in range(inputs.shape[0]):
> assert pytest.approx(out["z1"][i], rel=1e-8) == (
-75.26111111111476
- 8.815277777775934 * inputs["x1"][i]
+ 18.81527777777826 * inputs["x2"][i]
- 2.2556956302821618e-13 * (inputs["x2"][i] * inputs["x1"][i])
)
E assert -38.372222222227634 ± 3.8e-07 == -95.26111111112031
E comparison failed
E Obtained: -95.26111111112031
E Expected: -38.372222222227634 ± 3.8e-07
core/surrogate/tests/test_pysmo_surrogate.py:1357: AssertionError
---------------------------- Captured stdout setup -----------------------------
===========================Polynomial Regression===============================================
Warning: solution.pickle already exists; previous file will be overwritten.
No iterations will be run.
Default parameter estimation method is used.
Parameter estimation method: pyomo
max_fraction_training_samples set at 0.5
Number of adaptive samples (no_adaptive_samples) set at 4
Maximum number of iterations (Max_iter) set at: 0
Initial surrogate model is of order 1 with a cross-val error of 0.000000
Initial Regression Model Performance:
Order: 1 / MAE: 0.000000 / MSE: 0.000000 / R^2: 1.000000 / Adjusted R^2: 1.000000
Polynomial regression generates a good surrogate model for the input data.
-------------------------------------------------
-------------------------------------------------
Best solution found:
Order: 1 / MAE: 0.000000 / MSE: 0.000000 / R_sq: 1.000000 / Adjusted R^2: 1.000000
------------------------------------------------------------
The final coefficients of the regression terms are:
k | -18.372222
(x_ 1 )^ 1 | 5.406944
(x_ 2 )^ 1 | 4.593056
x_ 1 .x_ 2 | -0.0
Results saved in solution.pickle
2022-05-03 20:00:30 [INFO] idaes.core.surrogate.pysmo_surrogate: Model for output z1 trained successfully
------------------------------ Captured log setup ------------------------------
INFO idaes.core.surrogate.pysmo_surrogate:pysmo_surrogate.py:225 Model for output z1 trained successfully
___________ TestPysmoSurrogate.test_populate_block_unisurrogate_poly ___________
self = <idaes.core.surrogate.tests.test_pysmo_surrogate.TestPysmoSurrogate object at 0xffff725f30a0>
pysmo_surr1 = <idaes.core.surrogate.pysmo_surrogate.PysmoSurrogate object at 0xffff8a7bca30>
@pytest.mark.unit
def test_populate_block_unisurrogate_poly(self, pysmo_surr1):
# Test ``populate_block`` for one output with interaction terms
blk = SurrogateBlock(concrete=True)
blk.build_model(pysmo_surr1)
blk.display()
assert isinstance(blk.inputs, Var)
assert blk.inputs["x1"].bounds == (0, 5)
assert blk.inputs["x2"].bounds == (0, 10)
assert isinstance(blk.outputs, Var)
assert blk.outputs["z1"].bounds == (None, None)
assert isinstance(blk.pysmo_constraint, Constraint)
assert len(blk.pysmo_constraint) == 1
> assert str(blk.pysmo_constraint["z1"].body) == (
"outputs[z1] - (-75.26111111111476 - 8.815277777775934*inputs[x1] + 18.81527777777826*inputs[x2] - 2.2556956302821618e-13*(inputs[x2]*inputs[x1]))"
)
E AssertionError: assert 'outputs[z1] ...*inputs[x1]))' == 'outputs[z1] ...*inputs[x1]))'
E - outputs[z1] - (-75.26111111111476 - 8.815277777775934*inputs[x1] + 18.81527777777826*inputs[x2] - 2.2556956302821618e-13*(inputs[x2]*inputs[x1]))
E + outputs[z1] - (-18.37222222222536 + 5.406944444444783*inputs[x1] + 4.593055555556168*inputs[x2] - 9.323097849289752e-14*(inputs[x2]*inputs[x1]))
core/surrogate/tests/test_pysmo_surrogate.py:1379: AssertionError
---------------------------- Captured stdout setup -----------------------------
===========================Polynomial Regression===============================================
Warning: solution.pickle already exists; previous file will be overwritten.
No iterations will be run.
Default parameter estimation method is used.
Parameter estimation method: pyomo
max_fraction_training_samples set at 0.5
Number of adaptive samples (no_adaptive_samples) set at 4
Maximum number of iterations (Max_iter) set at: 0
Initial surrogate model is of order 1 with a cross-val error of 0.000000
Initial Regression Model Performance:
Order: 1 / MAE: 0.000000 / MSE: 0.000000 / R^2: 1.000000 / Adjusted R^2: 1.000000
Polynomial regression generates a good surrogate model for the input data.
-------------------------------------------------
-------------------------------------------------
Best solution found:
Order: 1 / MAE: 0.000000 / MSE: 0.000000 / R_sq: 1.000000 / Adjusted R^2: 1.000000
------------------------------------------------------------
The final coefficients of the regression terms are:
k | -18.372222
(x_ 1 )^ 1 | 5.406944
(x_ 2 )^ 1 | 4.593056
x_ 1 .x_ 2 | -0.0
Results saved in solution.pickle
2022-05-03 20:00:30 [INFO] idaes.core.surrogate.pysmo_surrogate: Model for output z1 trained successfully
------------------------------ Captured log setup ------------------------------
INFO idaes.core.surrogate.pysmo_surrogate:pysmo_surrogate.py:225 Model for output z1 trained successfully
----------------------------- Captured stdout call -----------------------------
Setting bound of inputs[x1] to (0, 5).
Setting bound of inputs[x2] to (0, 10).
Model _ScalarSurrogateBlock
Variables:
inputs : Size=2, Index=inputs_set
Key : Lower : Value : Upper : Fixed : Stale : Domain
x1 : 0 : 0 : 5 : False : False : Reals
x2 : 0 : 0 : 10 : False : False : Reals
outputs : Size=1, Index=outputs_set
Key : Lower : Value : Upper : Fixed : Stale : Domain
z1 : None : 0 : None : False : False : Reals
Objectives:
None
Constraints:
pysmo_constraint : Size=1
Key : Lower : Body : Upper
z1 : 0.0 : 18.37222222222536 : 0.0
_____________ TestPysmoSurrogate.test_evaluate_multisurrogate_poly _____________
self = <idaes.core.surrogate.tests.test_pysmo_surrogate.TestPysmoSurrogate object at 0xffff721cbb50>
pysmo_surr2_poly = (<idaes.core.surrogate.pysmo_surrogate.PysmoTrainedSurrogate object at 0xffff68660d00>, <idaes.core.surrogate.pysmo_surrogate.PysmoSurrogate object at 0xffff68660220>)
@pytest.mark.unit
def test_evaluate_multisurrogate_poly(self, pysmo_surr2_poly):
# Test ``evaluate_surrogate`` for multiple output polynomials with interaction terms
x = [
-2,
-1.8,
-1.6,
-1.4,
-1.2,
-1.0,
-0.8,
-0.6,
-0.4,
-0.2,
0,
0.2,
0.4,
0.6,
0.8,
1.0,
1.2,
1.4,
1.6,
1.8,
2.0,
]
inputs = np.array([np.tile(x, len(x)), np.repeat(x, len(x))])
inputs = pd.DataFrame(inputs.transpose(), columns=["x1", "x2"])
_, poly_trained = pysmo_surr2_poly
out = poly_trained.evaluate_surrogate(inputs)
for i in range(inputs.shape[0]):
> assert pytest.approx(out["z1"][i], rel=1e-8) == (
-75.26111111111476
- 8.815277777775934 * inputs["x1"][i]
+ 18.81527777777826 * inputs["x2"][i]
- 2.2556956302821618e-13 * (inputs["x2"][i] * inputs["x1"][i])
)
E assert -38.372222222227634 ± 3.8e-07 == -95.26111111112031
E comparison failed
E Obtained: -95.26111111112031
E Expected: -38.372222222227634 ± 3.8e-07
core/surrogate/tests/test_pysmo_surrogate.py:1416: AssertionError
---------------------------- Captured stdout setup -----------------------------
===========================Polynomial Regression===============================================
Warning: solution.pickle already exists; previous file will be overwritten.
No iterations will be run.
Default parameter estimation method is used.
Parameter estimation method: pyomo
max_fraction_training_samples set at 0.5
Number of adaptive samples (no_adaptive_samples) set at 4
Maximum number of iterations (Max_iter) set at: 0
Initial surrogate model is of order 1 with a cross-val error of 0.000000
Initial Regression Model Performance:
Order: 1 / MAE: 0.000000 / MSE: 0.000000 / R^2: 1.000000 / Adjusted R^2: 1.000000
Polynomial regression generates a good surrogate model for the input data.
-------------------------------------------------
-------------------------------------------------
Best solution found:
Order: 1 / MAE: 0.000000 / MSE: 0.000000 / R_sq: 1.000000 / Adjusted R^2: 1.000000
------------------------------------------------------------
The final coefficients of the regression terms are:
k | -18.372222
(x_ 1 )^ 1 | 5.406944
(x_ 2 )^ 1 | 4.593056
x_ 1 .x_ 2 | -0.0
Results saved in solution.pickle
2022-05-03 20:00:30 [INFO] idaes.core.surrogate.pysmo_surrogate: Model for output z1 trained successfully
===========================Polynomial Regression===============================================
Warning: solution.pickle already exists; previous file will be overwritten.
No iterations will be run.
Default parameter estimation method is used.
Parameter estimation method: pyomo
max_fraction_training_samples set at 0.5
Number of adaptive samples (no_adaptive_samples) set at 4
Maximum number of iterations (Max_iter) set at: 0
Initial surrogate model is of order 1 with a cross-val error of 0.000000
Initial Regression Model Performance:
Order: 1 / MAE: 0.000000 / MSE: 0.000000 / R^2: 1.000000 / Adjusted R^2: 1.000000
Polynomial regression generates a good surrogate model for the input data.
-------------------------------------------------
-------------------------------------------------
Best solution found:
Order: 1 / MAE: 0.000000 / MSE: 0.000000 / R_sq: 1.000000 / Adjusted R^2: 1.000000
------------------------------------------------------------
The final coefficients of the regression terms are:
k | 1.942932
(x_ 1 )^ 1 | 1.485733
(x_ 2 )^ 1 | 0.514267
x_ 1 .x_ 2 | 0.0
Results saved in solution.pickle
2022-05-03 20:00:30 [INFO] idaes.core.surrogate.pysmo_surrogate: Model for output z2 trained successfully
------------------------------ Captured log setup ------------------------------
INFO idaes.core.surrogate.pysmo_surrogate:pysmo_surrogate.py:225 Model for output z1 trained successfully
INFO idaes.core.surrogate.pysmo_surrogate:pysmo_surrogate.py:225 Model for output z2 trained successfully
__________ TestPysmoSurrogate.test_populate_block_multisurrogate_poly __________
self = <idaes.core.surrogate.tests.test_pysmo_surrogate.TestPysmoSurrogate object at 0xffff721c1b80>
pysmo_surr2_poly = (<idaes.core.surrogate.pysmo_surrogate.PysmoTrainedSurrogate object at 0xffff8aa9b310>, <idaes.core.surrogate.pysmo_surrogate.PysmoSurrogate object at 0xffff72eea4f0>)
@pytest.mark.unit
def test_populate_block_multisurrogate_poly(self, pysmo_surr2_poly):
# Test ``populate_block`` for multiple output polynomials with interaction terms
blk = SurrogateBlock(concrete=True)
(
_,
poly_trained,
) = pysmo_surr2_poly
blk.build_model(poly_trained)
assert isinstance(blk.inputs, Var)
assert blk.inputs["x1"].bounds == (None, None)
assert blk.inputs["x2"].bounds == (None, None)
assert isinstance(blk.outputs, Var)
assert blk.outputs["z1"].bounds == (None, None)
assert blk.outputs["z2"].bounds == (None, None)
assert isinstance(blk.pysmo_constraint, Constraint)
assert len(blk.pysmo_constraint) == 2
> assert str(blk.pysmo_constraint["z1"].body) == (
"outputs[z1] - (-75.26111111111476 - 8.815277777775934*inputs[x1] + 18.81527777777826*inputs[x2] - 2.2556956302821618e-13*(inputs[x2]*inputs[x1]))"
)
E AssertionError: assert 'outputs[z1] ...*inputs[x1]))' == 'outputs[z1] ...*inputs[x1]))'
E - outputs[z1] - (-75.26111111111476 - 8.815277777775934*inputs[x1] + 18.81527777777826*inputs[x2] - 2.2556956302821618e-13*(inputs[x2]*inputs[x1]))
E + outputs[z1] - (-18.37222222222536 + 5.406944444444783*inputs[x1] + 4.593055555556168*inputs[x2] - 9.323097849289752e-14*(inputs[x2]*inputs[x1]))
core/surrogate/tests/test_pysmo_surrogate.py:1448: AssertionError
---------------------------- Captured stdout setup -----------------------------
===========================Polynomial Regression===============================================
Warning: solution.pickle already exists; previous file will be overwritten.
No iterations will be run.
Default parameter estimation method is used.
Parameter estimation method: pyomo
max_fraction_training_samples set at 0.5
Number of adaptive samples (no_adaptive_samples) set at 4
Maximum number of iterations (Max_iter) set at: 0
Initial surrogate model is of order 1 with a cross-val error of 0.000000
Initial Regression Model Performance:
Order: 1 / MAE: 0.000000 / MSE: 0.000000 / R^2: 1.000000 / Adjusted R^2: 1.000000
Polynomial regression generates a good surrogate model for the input data.
-------------------------------------------------
-------------------------------------------------
Best solution found:
Order: 1 / MAE: 0.000000 / MSE: 0.000000 / R_sq: 1.000000 / Adjusted R^2: 1.000000
------------------------------------------------------------
The final coefficients of the regression terms are:
k | -18.372222
(x_ 1 )^ 1 | 5.406944
(x_ 2 )^ 1 | 4.593056
x_ 1 .x_ 2 | -0.0
Results saved in solution.pickle
2022-05-03 20:00:30 [INFO] idaes.core.surrogate.pysmo_surrogate: Model for output z1 trained successfully
===========================Polynomial Regression===============================================
Warning: solution.pickle already exists; previous file will be overwritten.
No iterations will be run.
Default parameter estimation method is used.
Parameter estimation method: pyomo
max_fraction_training_samples set at 0.5
Number of adaptive samples (no_adaptive_samples) set at 4
Maximum number of iterations (Max_iter) set at: 0
Initial surrogate model is of order 1 with a cross-val error of 0.000000
Initial Regression Model Performance:
Order: 1 / MAE: 0.000000 / MSE: 0.000000 / R^2: 1.000000 / Adjusted R^2: 1.000000
Polynomial regression generates a good surrogate model for the input data.
-------------------------------------------------
-------------------------------------------------
Best solution found:
Order: 1 / MAE: 0.000000 / MSE: 0.000000 / R_sq: 1.000000 / Adjusted R^2: 1.000000
------------------------------------------------------------
The final coefficients of the regression terms are:
k | 1.942932
(x_ 1 )^ 1 | 1.485733
(x_ 2 )^ 1 | 0.514267
x_ 1 .x_ 2 | 0.0
Results saved in solution.pickle
2022-05-03 20:00:30 [INFO] idaes.core.surrogate.pysmo_surrogate: Model for output z2 trained successfully
------------------------------ Captured log setup ------------------------------
INFO idaes.core.surrogate.pysmo_surrogate:pysmo_surrogate.py:225 Model for output z1 trained successfully
INFO idaes.core.surrogate.pysmo_surrogate:pysmo_surrogate.py:225 Model for output z2 trained successfully
_________ TestPysmoSurrogate.test_evaluate_multisurrogate_poly_userdef _________
self = <idaes.core.surrogate.tests.test_pysmo_surrogate.TestPysmoSurrogate object at 0xffff721c1640>
pysmo_surr4 = <idaes.core.surrogate.pysmo_surrogate.PysmoSurrogate object at 0xffff6a78dc70>
@pytest.mark.unit
def test_evaluate_multisurrogate_poly_userdef(self, pysmo_surr4):
# Test ``evaluate_surrogate`` for multiple output polynomials with ratio-type user-defined features
x = [
-2,
-1.8,
-1.6,
-1.4,
-1.2,
-1.0,
-0.8,
-0.6,
-0.4,
-0.2,
0.2,
0.4,
0.6,
0.8,
1.0,
1.2,
1.4,
1.6,
1.8,
2.0,
2.2,
]
inputs = np.array([np.tile(x, len(x)), np.repeat(x, len(x))])
inputs = pd.DataFrame(inputs.transpose(), columns=["x1", "x2"])
out = pysmo_surr4.evaluate_surrogate(inputs)
for i in range(inputs.shape[0]):
> assert pytest.approx(out["z1"][i], rel=1e-8) == (
-110.15000000001504
- 17.53750000000189 * inputs["x1"][i]
+ 27.537500000006148 * inputs["x2"][i]
- 5.3967136315336006e-11 * (inputs["x1"][i] / inputs["x2"][i])
)
E assert -22.133323509263356 ± 2.2e-07 == -130.15000000007754
E comparison failed
E Obtained: -130.15000000007754
E Expected: -22.133323509263356 ± 2.2e-07
core/surrogate/tests/test_pysmo_surrogate.py:1601: AssertionError
---------------------------- Captured stdout setup -----------------------------
===========================Polynomial Regression===============================================
Warning: solution.pickle already exists; previous file will be overwritten.
No iterations will be run.
Default parameter estimation method is used.
Parameter estimation method: pyomo
No iterations will be run.
Best surrogate model is of order 1 with a cross-val S.S. Error of 0.000000
------------------------------------------------------------
The final coefficients of the regression terms are:
k | -2.133333
(x_ 1 )^ 1 | 9.466666
(x_ 2 )^ 1 | 0.533333
The coefficients of the extra terms in additional_regression_features are:
Coeff. additional_regression_features[ 1 ]: 8e-06
Regression model performance on training data:
Order: 1 / MAE: 0.000000 / MSE: 0.000000 / R^2: 1.000000
Results saved in solution.pickle
2022-05-03 20:00:30 [INFO] idaes.core.surrogate.pysmo_surrogate: Model for output z1 trained successfully
===========================Polynomial Regression===============================================
Warning: solution.pickle already exists; previous file will be overwritten.
No iterations will be run.
Default parameter estimation method is used.
Parameter estimation method: pyomo
No iterations will be run.
Best surrogate model is of order 1 with a cross-val S.S. Error of 0.000000
------------------------------------------------------------
The final coefficients of the regression terms are:
k | 0.088889
(x_ 1 )^ 1 | 1.022222
(x_ 2 )^ 1 | 0.977778
The coefficients of the extra terms in additional_regression_features are:
Coeff. additional_regression_features[ 1 ]: 0.0
Regression model performance on training data:
Order: 1 / MAE: 0.000000 / MSE: 0.000000 / R^2: 1.000000
Results saved in solution.pickle
2022-05-03 20:00:30 [INFO] idaes.core.surrogate.pysmo_surrogate: Model for output z2 trained successfully
------------------------------ Captured log setup ------------------------------
INFO idaes.core.surrogate.pysmo_surrogate:pysmo_surrogate.py:225 Model for output z1 trained successfully
INFO idaes.core.surrogate.pysmo_surrogate:pysmo_surrogate.py:225 Model for output z2 trained successfully
______ TestPysmoSurrogate.test_populate_block_multisurrogate_poly_userdef ______
self = <idaes.core.surrogate.tests.test_pysmo_surrogate.TestPysmoSurrogate object at 0xffff721c16a0>
pysmo_surr4 = <idaes.core.surrogate.pysmo_surrogate.PysmoSurrogate object at 0xffff6bb055b0>
@pytest.mark.unit
def test_populate_block_multisurrogate_poly_userdef(self, pysmo_surr4):
# Test ``populate_block`` for multiple output polynomials with interaction terms
blk = SurrogateBlock(concrete=True)
blk.build_model(pysmo_surr4)
assert isinstance(blk.inputs, Var)
assert blk.inputs["x1"].bounds == (None, None)
assert blk.inputs["x2"].bounds == (None, None)
assert isinstance(blk.outputs, Var)
assert blk.outputs["z1"].bounds == (None, None)
assert blk.outputs["z2"].bounds == (None, None)
assert isinstance(blk.pysmo_constraint, Constraint)
assert len(blk.pysmo_constraint) == 2
> assert str(blk.pysmo_constraint["z1"].body) == (
"outputs[z1] - (-110.15000000001504 - 17.53750000000189*inputs[x1] + 27.537500000006148*inputs[x2] - 5.3967136315336006e-11*(inputs[x1]/inputs[x2]))"
)
E AssertionError: assert 'outputs[z1] .../inputs[x2]))' == 'outputs[z1] .../inputs[x2]))'
E - outputs[z1] - (-110.15000000001504 - 17.53750000000189*inputs[x1] + 27.537500000006148*inputs[x2] - 5.3967136315336006e-11*(inputs[x1]/inputs[x2]))
E + outputs[z1] - (-2.1333333038658475 + 9.466666246499225*inputs[x1] + 0.5333330308951045*inputs[x2] + 8.34939114827528e-06*(inputs[x1]/inputs[x2]))
core/surrogate/tests/test_pysmo_surrogate.py:1629: AssertionError
---------------------------- Captured stdout setup -----------------------------
===========================Polynomial Regression===============================================
Warning: solution.pickle already exists; previous file will be overwritten.
No iterations will be run.
Default parameter estimation method is used.
Parameter estimation method: pyomo
No iterations will be run.
Best surrogate model is of order 1 with a cross-val S.S. Error of 0.000000
------------------------------------------------------------
The final coefficients of the regression terms are:
k | -2.133333
(x_ 1 )^ 1 | 9.466666
(x_ 2 )^ 1 | 0.533333
The coefficients of the extra terms in additional_regression_features are:
Coeff. additional_regression_features[ 1 ]: 8e-06
Regression model performance on training data:
Order: 1 / MAE: 0.000000 / MSE: 0.000000 / R^2: 1.000000
Results saved in solution.pickle
2022-05-03 20:00:31 [INFO] idaes.core.surrogate.pysmo_surrogate: Model for output z1 trained successfully
===========================Polynomial Regression===============================================
Warning: solution.pickle already exists; previous file will be overwritten.
No iterations will be run.
Default parameter estimation method is used.
Parameter estimation method: pyomo
No iterations will be run.
Best surrogate model is of order 1 with a cross-val S.S. Error of 0.000000
------------------------------------------------------------
The final coefficients of the regression terms are:
k | 0.088889
(x_ 1 )^ 1 | 1.022222
(x_ 2 )^ 1 | 0.977778
The coefficients of the extra terms in additional_regression_features are:
Coeff. additional_regression_features[ 1 ]: 0.0
Regression model performance on training data:
Order: 1 / MAE: 0.000000 / MSE: 0.000000 / R^2: 1.000000
Results saved in solution.pickle
2022-05-03 20:00:31 [INFO] idaes.core.surrogate.pysmo_surrogate: Model for output z2 trained successfully
------------------------------ Captured log setup ------------------------------
INFO idaes.core.surrogate.pysmo_surrogate:pysmo_surrogate.py:225 Model for output z1 trained successfully
INFO idaes.core.surrogate.pysmo_surrogate:pysmo_surrogate.py:225 Model for output z2 trained successfully
______________________ TestPysmoSurrogate.test_save_poly1 ______________________
self = <idaes.core.surrogate.tests.test_pysmo_surrogate.TestPysmoSurrogate object at 0xffff71cb7310>
pysmo_surr1 = <idaes.core.surrogate.pysmo_surrogate.PysmoSurrogate object at 0xffff6b8c24c0>
@pytest.mark.unit
def test_save_poly1(self, pysmo_surr1):
# Test save for polynomial regression with single output with bounds supplied
stream1 = StringIO()
pysmo_surr1.save(stream1)
> assert re.sub("errors.*?}", "", jstring_poly_1) == re.sub(
"errors.*?}", "", stream1.getvalue()
)
E assert '{"model_enco...ype": "poly"}' == '{"model_enco...ype": "poly"}'
E Skipping 149 identical leading characters in diff, use -v to show
E - rray": [[-18.37222222222536], [5.406944444444783], [4.593055555556168], [-9.323097849289752e-14]], "final_polynomial_order": 1, ", "extra_terms_feature_vector": ["IndexedParam[x1]", "IndexedParam[x2]"]}, "map": {"regression_data_columns": "list", "multinomials": "str", "additional_term_expressions": "list", "optimal_weights_array": "numpy", "final_polynomial_order": "str", "}}, "input_labels": ["x1", "x2"], "output_labels": ["z1"], "input_bounds": {"x1": [0, 5], "x2": [0, 10]}, "surrogate_type": "poly"}
E ?...
E
E ...Full output truncated (3 lines hidden), use '-vv' to show
core/surrogate/tests/test_pysmo_surrogate.py:2030: AssertionError
---------------------------- Captured stdout setup -----------------------------
===========================Polynomial Regression===============================================
Warning: solution.pickle already exists; previous file will be overwritten.
No iterations will be run.
Default parameter estimation method is used.
Parameter estimation method: pyomo
max_fraction_training_samples set at 0.5
Number of adaptive samples (no_adaptive_samples) set at 4
Maximum number of iterations (Max_iter) set at: 0
Initial surrogate model is of order 1 with a cross-val error of 0.000000
Initial Regression Model Performance:
Order: 1 / MAE: 0.000000 / MSE: 0.000000 / R^2: 1.000000 / Adjusted R^2: 1.000000
Polynomial regression generates a good surrogate model for the input data.
-------------------------------------------------
-------------------------------------------------
Best solution found:
Order: 1 / MAE: 0.000000 / MSE: 0.000000 / R_sq: 1.000000 / Adjusted R^2: 1.000000
------------------------------------------------------------
The final coefficients of the regression terms are:
k | -18.372222
(x_ 1 )^ 1 | 5.406944
(x_ 2 )^ 1 | 4.593056
x_ 1 .x_ 2 | -0.0
Results saved in solution.pickle
2022-05-03 20:00:31 [INFO] idaes.core.surrogate.pysmo_surrogate: Model for output z1 trained successfully
------------------------------ Captured log setup ------------------------------
INFO idaes.core.surrogate.pysmo_surrogate:pysmo_surrogate.py:225 Model for output z1 trained successfully
______________________ TestPysmoSurrogate.test_save_poly2 ______________________
self = <idaes.core.surrogate.tests.test_pysmo_surrogate.TestPysmoSurrogate object at 0xffff71cb7130>
pysmo_surr2_poly = (<idaes.core.surrogate.pysmo_surrogate.PysmoTrainedSurrogate object at 0xffff6bd43070>, <idaes.core.surrogate.pysmo_surrogate.PysmoSurrogate object at 0xffff6bd43250>)
@pytest.mark.unit
def test_save_poly2(self, pysmo_surr2_poly):
# Test save for polynomial regression with multiple outputs - most complicated to save
_, poly_trained = pysmo_surr2_poly
stream2a = StringIO()
poly_trained.save(stream2a)
> assert re.sub("errors.*?}", "", jstring_poly_2) == re.sub(
"errors.*?}", "", stream2a.getvalue()
)
E assert '{"model_enco...ype": "poly"}' == '{"model_enco...ype": "poly"}'
E Skipping 149 identical leading characters in diff, use -v to show
E - rray": [[-18.37222222222536], [5.406944444444783], [4.593055555556168], [-9.323097849289752e-14]], "final_polynomial_order": 1, ", "extra_terms_feature_vector": ["IndexedParam[x1]", "IndexedParam[x2]"]}, "map": {"regression_data_columns": "list", "multinomials": "str", "additional_term_expressions": "list", "optimal_weights_array": "numpy", "final_polynomial_order": "str", "}, "z2": {"attr": {"regression_data_columns": ["x1", "x2"], "multinomials": 1, "additional_term_expressions": [], "optimal_weights_a...
E
E ...Full output truncated (2 lines hidden), use '-vv' to show
core/surrogate/tests/test_pysmo_surrogate.py:2040: AssertionError
---------------------------- Captured stdout setup -----------------------------
===========================Polynomial Regression===============================================
Warning: solution.pickle already exists; previous file will be overwritten.
No iterations will be run.
Default parameter estimation method is used.
Parameter estimation method: pyomo
max_fraction_training_samples set at 0.5
Number of adaptive samples (no_adaptive_samples) ```
I think I found the issue. The tests that fail seem to have multiple solutions. It seems it's not a problem with Pysmo just that the tests have different equally good results. Here's an example from the first failure. pysmo_test.xlsx
Yeah, I suspected that was the problem given the small size of the training data and the fact that the input variables are strongly correlated. I will probably have to increase the training set size or define a different test problem entirely.
@OOAmusat Any update on this issue? I assume it is not making it into the August release at this point,
@andrewlee94 No updates for now. It'll be addressed, but not before the August release.
The problem is pretty straight forward and it would be nice if it didn't fail on ARM64. Maybe I can take a little time to see if there is a quick fix to make it so there is unique solution for the test problem.
Closing as fixed, although we are seeing some random failures for one test case (Kriging) - we suspect that this is due to random sampling which should be fixed.