PySMO: fix warnings

idaes/core/surrogate/pysmo/kriging.py

@@ -537,9 +537,10 @@ def error_calculation(theta, p, mean, cov_inv, y_mu, x, y_data):
         for i in range(0, x.shape[0]):
             cmt = (np.matmul(((np.abs(x[i, :] - x)) ** p), theta)).transpose()
             cov_matrix_tests = np.exp(-1 * cmt)
-            y_prediction[i, 0] = mean + np.matmul(
+            y_pred_val = mean + np.matmul(
                 np.matmul(cov_matrix_tests.transpose(), cov_inv), y_mu
             )
+            y_prediction[i, 0] = y_pred_val.item()
         ss_error = (1 / y_data.shape[0]) * (np.sum((y_data - y_prediction) ** 2))
         rmse_error = np.sqrt(ss_error)
         return ss_error, rmse_error, y_prediction
@@ -589,10 +590,11 @@ def predict_output(self, x_pred):
                 )
             ).transpose()
             cov_matrix_tests = np.exp(-1 * cmt)
-            y_pred[i, 0] = self.optimal_mean + np.matmul(
+            y_pred_val = self.optimal_mean + np.matmul(
                 np.matmul(cov_matrix_tests.transpose(), self.covariance_matrix_inverse),
                 self.optimal_y_mu,
             )
+            y_pred[i, 0] = y_pred_val.item()
         return y_pred
 
     def training(self):

idaes/core/surrogate/pysmo/polynomial_regression.py

@@ -15,7 +15,6 @@
 # pylint: disable=missing-function-docstring
 
 import os.path
-import warnings
 import pickle
 
 # Imports from third parties
@@ -42,7 +41,9 @@
 
 # Imports from IDAES namespace
 from idaes.core.surrogate.pysmo.utils import NumpyEvaluator
+import idaes.logger as idaeslog
 
+_log = idaeslog.getLogger(__name__)
 
 __author__ = "Oluwamayowa Amusat"
 
@@ -346,7 +347,7 @@ def __init__(
             print("The number of cross-validation cases (3) is used.")
             number_of_crossvalidations = 3
         elif number_of_crossvalidations > 10:
-            warnings.warn(
+            _log.warning(
                 "The number of cross-validations entered is large. The simulation may take a while to run"
             )
         self.number_of_crossvalidations = number_of_crossvalidations
@@ -356,7 +357,7 @@ def __init__(
             # pylint: disable-next=broad-exception-raised
             raise Exception("Maximum polynomial order must be an integer")
         elif maximum_polynomial_order > 10:
-            warnings.warn(
+            _log.warning(
                 "The maximum allowed polynomial order is 10. Value has been adjusted to 10."
             )
             maximum_polynomial_order = 10
@@ -1000,7 +1001,7 @@ def results_generation(self, beta, order):
         print("\n------------------------------------------------------------")
         print("The final coefficients of the regression terms are: \n")
         print("k               |", beta[0, 0])
-        results_df = pd.concat([results_df, pd.Series({"k": beta[0, 0]})], axis=0)
+        results_df = pd.Series({"k": beta[0, 0]})
         if self.multinomials == 1:
             for i in range(1, order + 1):
                 for j in range(1, self.number_of_x_vars + 1):
@@ -1449,9 +1450,7 @@ def polynomial_regression_fitting(self, additional_regression_features=None):
             if r_square_opt > 0.95:
                 self.fit_status = "ok"
             else:
-                warnings.warn(
-                    "Polynomial regression generates poor fit for the dataset"
-                )
+                _log.warning("Polynomial regression generates poor fit for the dataset")
                 self.fit_status = "poor"
 
             self.pickle_save({"model": self})
@@ -1551,9 +1550,7 @@ def polynomial_regression_fitting(self, additional_regression_features=None):
             if r_square > 0.95:
                 self.fit_status = "ok"
             else:
-                warnings.warn(
-                    "Polynomial regression generates poor fit for the dataset"
-                )
+                _log.warning("Polynomial regression generates poor fit for the dataset")
                 self.fit_status = "poor"
 
             self.pickle_save({"model": self})

idaes/core/surrogate/pysmo/radial_basis_function.py

@@ -18,7 +18,6 @@
 
 # Imports from the python standard library
 import os.path
-import warnings
 import pickle
 
 # Imports from third parties
@@ -43,6 +42,9 @@
 
 # Imports from IDAES namespace
 from idaes.core.surrogate.pysmo.sampling import FeatureScaling as fs
+import idaes.logger as idaeslog
+
+_log = idaeslog.getLogger(__name__)
 
 __author__ = "Oluwamayowa Amusat"
 
@@ -1105,7 +1107,7 @@ def training(self):
         if x_condition_number < (1 / np.finfo(float).eps):
             self.solution_status = "ok"
         else:
-            warnings.warn(
+            _log.warning(
                 "The parameter matrix A in A.x=B is ill-conditioned (condition number > 1e10). The solution returned may be inaccurate or unstable - inspect rmse error. Regularization (if not already done) may improve solution"
             )
             self.solution_status = "unstable solution"

idaes/core/surrogate/pysmo/sampling.py

@@ -15,11 +15,13 @@
 # pylint: disable=missing-class-docstring
 # pylint: disable=missing-function-docstring
 
-import warnings
 import itertools
 
 import numpy as np
 import pandas as pd
+import idaes.logger as idaeslog
+
+_log = idaeslog.getLogger(__name__)
 
 __author__ = "Oluwamayowa Amusat"
 
@@ -180,7 +182,7 @@ def sample_point_selection(self, full_data, sample_points, sampling_type):
 
             unique_sample_points = np.unique(points_closest_unscaled, axis=0)
             if unique_sample_points.shape[0] < points_closest_unscaled.shape[0]:
-                warnings.warn(
+                _log.warning(
                     "The returned number of samples is less than the requested number due to repetitions during nearest neighbour selection."
                 )
             print(
@@ -388,7 +390,7 @@ def selection_columns_preprocessing(self, data_input, xlabels, ylabels):
                     warn_str = "The following columns were dropped: " + str(
                         dropped_cols
                     )
-                    warnings.warn(warn_str)
+                    _log.warning(warn_str)
                 self.x_data = data_input.filter(xlabels).values
                 self.data_headers = set_of_labels
                 self.data_headers_xvars = xlabels
@@ -448,7 +450,7 @@ def selection_columns_preprocessing(self, data_input, xlabels, ylabels):
                     warn_str = "The following columns were dropped: " + str(
                         dropped_cols
                     )
-                    warnings.warn(warn_str)
+                    _log.warning(warn_str)
                 self.x_data = data_input[:, xlabels]
                 self.data_headers = set_of_labels
                 self.data_headers_xvars = xlabels
@@ -1417,7 +1419,7 @@ def __init__(
         elif tolerance > 0.1:
             raise ValueError("Tolerance must be less than 0.1 to achieve good results")
         elif tolerance < 1e-9:
-            warnings.warn(
+            _log.warning(
                 "Tolerance too tight. CVT algorithm may take long time to converge."
             )
         elif (tolerance < 0.1) and (tolerance > 1e-9):
@@ -1784,7 +1786,7 @@ def generate_from_dist(self, dist_name):
                     )
                     > 0
                 ):
-                    warnings.warn(
+                    _log.warning(
                         "Points adjusted to remain within specified Gaussian bounds. This may affect the underlying distribution."
                     )
                     out_locations = [
@@ -1796,7 +1798,7 @@ def generate_from_dist(self, dist_name):
                         rep_value = var_values[k]
                         while (rep_value < 0) or (rep_value > 1):
                             rep_value = dist(loc=0.5, scale=1 / 6, size=1)
-                        var_values[k] = rep_value
+                        var_values[k] = rep_value[0]
                 assert (
                     sum([1 for i in range(0, var_values.shape[0]) if var_values[i] > 1])
                     + sum(

idaes/core/surrogate/pysmo/tests/test_kriging.py

@@ -97,6 +97,9 @@ def test__init__07(self, array_type):
 
     @pytest.mark.unit
     @pytest.mark.parametrize("array_type", [np.array, pd.DataFrame])
+    @pytest.mark.filterwarnings(
+        "ignore:invalid value encountered in log:RuntimeWarning"
+    )
     def test__init__08(self, array_type):
         input_array = array_type(self.test_data)
         file_name = "test_filename.pickle"
@@ -107,6 +110,9 @@ def test__init__08(self, array_type):
 
     @pytest.mark.unit
     @pytest.mark.parametrize("array_type", [np.array, pd.DataFrame])
+    @pytest.mark.filterwarnings(
+        "ignore:invalid value encountered in log:RuntimeWarning"
+    )
     def test__init__09(self, array_type):
         input_array = array_type(self.test_data)
         file_name1 = "test_filename1.pickle"
@@ -343,9 +349,10 @@ def test_error_calculation(self, array_type):
                 )
             ).transpose()
             cov_matrix_tests = np.exp(-1 * cmt)
-            y_prediction_exp[i, 0] = mean + np.matmul(
+            y_prediction_val = mean + np.matmul(
                 np.matmul(cov_matrix_tests.transpose(), cov_inv), y_mu
             )
+            y_prediction_exp[i, 0] = y_prediction_val.item()
 
         ss_error, rmse_error, y_prediction = KrigingClass.error_calculation(
             theta,
@@ -402,6 +409,9 @@ def test_r2_calculation(self, array_type):
 
     @pytest.mark.unit
     @pytest.mark.parametrize("array_type", [np.array, pd.DataFrame])
+    @pytest.mark.filterwarnings(
+        "ignore:invalid value encountered in log:RuntimeWarning"
+    )
     def test_predict_output_01(self, array_type):
         input_array = array_type(self.training_data)
         np.random.seed(0)
@@ -412,6 +422,9 @@ def test_predict_output_01(self, array_type):
 
     @pytest.mark.unit
     @pytest.mark.parametrize("array_type", [np.array, pd.DataFrame])
+    @pytest.mark.filterwarnings(
+        "ignore:invalid value encountered in log:RuntimeWarning"
+    )
     def test_predict_output(self, array_type):
         input_array = array_type(self.training_data)
         np.random.seed(0)

idaes/core/surrogate/pysmo/tests/test_polynomial_regression.py

@@ -20,6 +20,7 @@
 import numpy as np
 import pandas as pd
 import pytest
+import idaes.logger as idaeslog
 
 
 class TestFeatureScaling:
@@ -300,19 +301,23 @@ def test__init__08(self, array_type1, array_type2):
     @pytest.mark.unit
     @pytest.mark.parametrize("array_type1", [np.array, pd.DataFrame])
     @pytest.mark.parametrize("array_type2", [np.array, pd.DataFrame])
-    def test__init__09(self, array_type1, array_type2):
+    def test__init__09(self, array_type1, array_type2, caplog):
+        caplog.set_level(idaeslog.WARNING)
+        warning_msg = "The number of cross-validations entered is large. The simulation may take a while to run"
         original_data_input = array_type1(self.test_data)
         regression_data_input = array_type2(self.sample_points)
-        with pytest.warns(Warning):
-            PolyClass = PolynomialRegression(
-                original_data_input,
-                regression_data_input,
-                maximum_polynomial_order=5,
-                number_of_crossvalidations=11,
-            )
-            assert (
-                PolyClass.number_of_crossvalidations == 11
-            )  # Default number of cross-validations
+        PolyClass = PolynomialRegression(
+            original_data_input,
+            regression_data_input,
+            maximum_polynomial_order=5,
+            number_of_crossvalidations=11,
+        )
+        assert warning_msg in caplog.text
+        for record in caplog.records:
+            assert record.levelno == idaeslog.WARNING
+        assert (
+            PolyClass.number_of_crossvalidations == 11
+        )  # Default number of cross-validations
 
     @pytest.mark.unit
     @pytest.mark.parametrize("array_type1", [np.array, pd.DataFrame])
@@ -328,14 +333,23 @@ def test__init__10(self, array_type1, array_type2):
     @pytest.mark.unit
     @pytest.mark.parametrize("array_type1", [np.array, pd.DataFrame])
     @pytest.mark.parametrize("array_type2", [np.array, pd.DataFrame])
-    def test__init__11(self, array_type1, array_type2):
+    def test__init__11(self, array_type1, array_type2, caplog):
+        caplog.set_level(idaeslog.WARNING)
+        warning_msg = (
+            "The maximum allowed polynomial order is 10. Value has been adjusted to 10."
+        )
         original_data_input = array_type1(self.test_data_large)
         regression_data_input = array_type2(self.sample_points_large)
-        with pytest.warns(Warning):
-            PolyClass = PolynomialRegression(
-                original_data_input, regression_data_input, maximum_polynomial_order=11
-            )
-            assert PolyClass.max_polynomial_order == 10
+        PolyClass = PolynomialRegression(
+            original_data_input, regression_data_input, maximum_polynomial_order=11
+        )
+        warning_msg = (
+            "The maximum allowed polynomial order is 10. Value has been adjusted to 10."
+        )
+        assert warning_msg in caplog.text
+        for record in caplog.records:
+            assert record.levelno == idaeslog.WARNING
+        assert PolyClass.max_polynomial_order == 10
 
     @pytest.mark.unit
     @pytest.mark.parametrize("array_type1", [np.array, pd.DataFrame])
@@ -1645,17 +1659,12 @@ def test_results_generation_01(self, array_type1, array_type2):
         beta = np.array([[0], [0], [0]])
         expected_df = pd.Series()
         row_list = np.array([["k"], ["(x_1)^1"], ["(x_2)^1"]])
-        expected_df = pd.concat(
-            [
-                expected_df,
-                pd.Series(
-                    {
-                        row_list[0, 0]: beta[0, 0],
-                        row_list[1, 0]: beta[1, 0],
-                        row_list[2, 0]: beta[2, 0],
-                    }
-                ),
-            ]
+        expected_df = pd.Series(
+            {
+                row_list[0, 0]: beta[0, 0],
+                row_list[1, 0]: beta[1, 0],
+                row_list[2, 0]: beta[2, 0],
+            }
         )
         output_df = data_feed.results_generation(beta, order)
         assert output_df.index.to_list() == expected_df.index.to_list()
@@ -1687,21 +1696,16 @@ def test_results_generation_02(self, array_type1, array_type2):
                 ["(x_2)^3"],
             ]
         )
-        expected_df = pd.concat(
-            [
-                expected_df,
-                pd.Series(
-                    {
-                        row_list[0, 0]: beta[0, 0],
-                        row_list[1, 0]: beta[1, 0],
-                        row_list[2, 0]: beta[2, 0],
-                        row_list[3, 0]: beta[3, 0],
-                        row_list[4, 0]: beta[4, 0],
-                        row_list[5, 0]: beta[5, 0],
-                        row_list[6, 0]: beta[6, 0],
-                    }
-                ),
-            ]
+        expected_df = pd.Series(
+            {
+                row_list[0, 0]: beta[0, 0],
+                row_list[1, 0]: beta[1, 0],
+                row_list[2, 0]: beta[2, 0],
+                row_list[3, 0]: beta[3, 0],
+                row_list[4, 0]: beta[4, 0],
+                row_list[5, 0]: beta[5, 0],
+                row_list[6, 0]: beta[6, 0],
+            }
         )
         output_df = data_feed.results_generation(beta, order)
         assert output_df.index.to_list() == expected_df.index.to_list()
@@ -1725,20 +1729,15 @@ def test_results_generation_03(self, array_type1, array_type2):
         row_list = np.array(
             [["k"], ["(x_1)^1"], ["(x_2)^1"], ["(x_1)^2"], ["(x_2)^2"], ["(x_1).(x_2)"]]
         )
-        expected_df = pd.concat(
-            [
-                expected_df,
-                pd.Series(
-                    {
-                        row_list[0, 0]: beta[0, 0],
-                        row_list[1, 0]: beta[1, 0],
-                        row_list[2, 0]: beta[2, 0],
-                        row_list[3, 0]: beta[3, 0],
-                        row_list[4, 0]: beta[4, 0],
-                        row_list[5, 0]: beta[5, 0],
-                    }
-                ),
-            ]
+        expected_df = pd.Series(
+            {
+                row_list[0, 0]: beta[0, 0],
+                row_list[1, 0]: beta[1, 0],
+                row_list[2, 0]: beta[2, 0],
+                row_list[3, 0]: beta[3, 0],
+                row_list[4, 0]: beta[4, 0],
+                row_list[5, 0]: beta[5, 0],
+            }
         )
         output_df = data_feed.results_generation(beta, order)
         assert output_df.index.to_list() == expected_df.index.to_list()