fix: 🧪 Fix tests with new scikit-learn version

2025-08-15 15:35:52 +00:00 · 2023-01-14 21:31:34 +01:00
parent 7ef88bd5c7
commit 6dc3a59df8
13 changed files with 35 additions and 34 deletions
--- a/benchmark/scripts/be_build_grid.py
+++ b/benchmark/scripts/be_build_grid.py
@@ -46,7 +46,7 @@ def main(args_test=None):
        '{"C": 7, "gamma": 0.1, "kernel": "rbf", "multiclass_strategy": '
        '"ovr"}',
        '{"C": 5, "kernel": "rbf", "gamma": "auto"}',
-        '{"C": 0.05, "max_iter": 10000.0, "kernel": "liblinear", '
+        '{"C": 0.05, "max_iter": 10000, "kernel": "liblinear", '
        '"multiclass_strategy": "ovr"}',
        '{"C":0.0275, "kernel": "liblinear", "multiclass_strategy": "ovr"}',
        '{"C": 7, "gamma": 10.0, "kernel": "rbf", "multiclass_strategy": '
@@ -97,7 +97,7 @@ def main(args_test=None):
    for item in results:
        results_tmp = {"n_jobs": [-1], "n_estimators": [100]}
        for key, value in results[item].items():
-            new_key = f"base_estimator__{key}"
+            new_key = f"estimator__{key}"
            try:
                results_tmp[new_key] = sorted(value)
            except TypeError:
@@ -111,6 +111,7 @@ def main(args_test=None):
                t2 = sorted([x for x in value if isinstance(x, str)])
                results_tmp[new_key] = t1 + t2
        output.append(results_tmp)
+
    # save results
    file_name = Files.grid_input(args.score, args.model)
    file_output = os.path.join(Folders.results, file_name)
--- a/benchmark/tests/BestResults_test.py
+++ b/benchmark/tests/BestResults_test.py
@@ -18,7 +18,7 @@ class BestResultTest(TestBase):
                    "C": 7,
                    "gamma": 0.1,
                    "kernel": "rbf",
-                    "max_iter": 10000.0,
+                    "max_iter": 10000,
                    "multiclass_strategy": "ovr",
                },
                "results_accuracy_STree_iMac27_2021-09-30_11:42:07_0.json",
--- a/benchmark/tests/Experiment_test.py
+++ b/benchmark/tests/Experiment_test.py
@@ -52,7 +52,7 @@ class ExperimentTest(TestBase):
                    "C": 7,
                    "gamma": 0.1,
                    "kernel": "rbf",
-                    "max_iter": 10000.0,
+                    "max_iter": 10000,
                    "multiclass_strategy": "ovr",
                },
                "results_accuracy_STree_iMac27_2021-09-30_11:42:07_0.json",
--- a/benchmark/tests/results/best_results_accuracy_STree.json
+++ b/benchmark/tests/results/best_results_accuracy_STree.json
@@ -1 +1 @@
-{"balance-scale": [0.98, {"splitter": "best", "max_features": "auto"}, "results_accuracy_STree_iMac27_2021-10-27_09:40:40_0.json"], "balloons": [0.86, {"C": 7, "gamma": 0.1, "kernel": "rbf", "max_iter": 10000.0, "multiclass_strategy": "ovr"}, "results_accuracy_STree_iMac27_2021-09-30_11:42:07_0.json"]}
+{"balance-scale": [0.98, {"splitter": "best", "max_features": "auto"}, "results_accuracy_STree_iMac27_2021-10-27_09:40:40_0.json"], "balloons": [0.86, {"C": 7, "gamma": 0.1, "kernel": "rbf", "max_iter": 10000, "multiclass_strategy": "ovr"}, "results_accuracy_STree_iMac27_2021-09-30_11:42:07_0.json"]}
--- a/benchmark/tests/results/results_accuracy_STree_iMac27_2021-09-30_11:42:07_0.json
+++ b/benchmark/tests/results/results_accuracy_STree_iMac27_2021-09-30_11:42:07_0.json
@@ -17,10 +17,10 @@
      "features": 4,
      "classes": 3,
      "hyperparameters": {
-        "C": 10000.0,
+        "C": 10000,
        "gamma": 0.1,
        "kernel": "rbf",
-        "max_iter": 10000.0,
+        "max_iter": 10000,
        "multiclass_strategy": "ovr"
      },
      "nodes": 7.0,
@@ -40,7 +40,7 @@
        "C": 7,
        "gamma": 0.1,
        "kernel": "rbf",
-        "max_iter": 10000.0,
+        "max_iter": 10000,
        "multiclass_strategy": "ovr"
      },
      "nodes": 3.0,
--- a/benchmark/tests/test_files/be_build_grid.test
+++ b/benchmark/tests/test_files/be_build_grid.test
@@ -6,13 +6,13 @@
        "n_estimators": [
            100
        ],
-        "base_estimator__C": [
+        "estimator__C": [
            1.0
        ],
-        "base_estimator__kernel": [
+        "estimator__kernel": [
            "linear"
        ],
-        "base_estimator__multiclass_strategy": [
+        "estimator__multiclass_strategy": [
            "ovo"
        ]
    },
@@ -23,7 +23,7 @@
        "n_estimators": [
            100
        ],
-        "base_estimator__C": [
+        "estimator__C": [
            0.001,
            0.0275,
            0.05,
@@ -36,10 +36,10 @@
            7,
            10000.0
        ],
-        "base_estimator__kernel": [
+        "estimator__kernel": [
            "liblinear"
        ],
-        "base_estimator__multiclass_strategy": [
+        "estimator__multiclass_strategy": [
            "ovr"
        ]
    },
@@ -50,7 +50,7 @@
        "n_estimators": [
            100
        ],
-        "base_estimator__C": [
+        "estimator__C": [
            0.05,
            1.0,
            1.05,
@@ -62,7 +62,7 @@
            57,
            10000.0
        ],
-        "base_estimator__gamma": [
+        "estimator__gamma": [
            0.001,
            0.1,
            0.14,
@@ -70,10 +70,10 @@
            "auto",
            "scale"
        ],
-        "base_estimator__kernel": [
+        "estimator__kernel": [
            "rbf"
        ],
-        "base_estimator__multiclass_strategy": [
+        "estimator__multiclass_strategy": [
            "ovr"
        ]
    },
@@ -84,20 +84,20 @@
        "n_estimators": [
            100
        ],
-        "base_estimator__C": [
+        "estimator__C": [
            0.05,
            0.2,
            1.0,
            8.25
        ],
-        "base_estimator__gamma": [
+        "estimator__gamma": [
            0.1,
            "scale"
        ],
-        "base_estimator__kernel": [
+        "estimator__kernel": [
            "poly"
        ],
-        "base_estimator__multiclass_strategy": [
+        "estimator__multiclass_strategy": [
            "ovo",
            "ovr"
        ]
--- a/benchmark/tests/test_files/be_main_best.test
+++ b/benchmark/tests/test_files/be_main_best.test
@@ -9,7 +9,7 @@
 Dataset                        Sampl. Feat. Cls Nodes   Leaves  Depth   Score           Time              Hyperparameters 
 ============================== ====== ===== === ======= ======= ======= =============== ================= =============== 
 [96mbalance-scale                      625     4   3   23.32   12.16    6.44 0.840160±0.0304   0.013745±0.0019 {'splitter': 'best', 'max_features': 'auto'} 
-[94mballoons                            16     4   2    3.00    2.00    2.00 0.860000±0.2850   0.000388±0.0000 {'C': 7, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000.0, 'multiclass_strategy': 'ovr'} 
+[94mballoons                            16     4   2    3.00    2.00    2.00 0.860000±0.2850   0.000388±0.0000 {'C': 7, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000, 'multiclass_strategy': 'ovr'} 
 [94m*************************************************************************************************************************
 [94m* accuracy compared to STree_default (liblinear-ovr) .:  0.0422                                                         *
 [94m*************************************************************************************************************************
--- a/benchmark/tests/test_files/excel.test
+++ b/benchmark/tests/test_files/excel.test
@@ -32,7 +32,7 @@
 7;9;"0.0150468069702512"
 7;10;"0.01404867172241211"
 7;11;"0.002026269126958884"
-7;12;"{'C': 10000.0, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000.0, 'multiclass_strategy': 'ovr'}"
+7;12;"{'C': 10000, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000, 'multiclass_strategy': 'ovr'}"
 8;1;"balloons"
 8;2;"16"
 8;3;"4"
@@ -44,5 +44,5 @@
 8;9;"0.2850146195080759"
 8;10;"0.0008541679382324218"
 8;11;"3.629469326417878e-05"
-8;12;"{'C': 7, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000.0, 'multiclass_strategy': 'ovr'}"
+8;12;"{'C': 7, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000, 'multiclass_strategy': 'ovr'}"
 10;1;"** accuracy compared to STree_default (liblinear-ovr) .:  0.0454"
--- a/benchmark/tests/test_files/excel_compared.test
+++ b/benchmark/tests/test_files/excel_compared.test
@@ -32,7 +32,7 @@
 7;10;0.0150468069702512
 7;11;0.01404867172241211
 7;12;0.002026269126958884
-7;13;"{'C': 10000.0, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000.0, 'multiclass_strategy': 'ovr'}"
+7;13;"{'C': 10000, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000, 'multiclass_strategy': 'ovr'}"
 8;1;"balloons"
 8;2;16
 8;3;4
@@ -45,7 +45,7 @@
 8;10;0.2850146195080759
 8;11;0.0008541679382324218
 8;12;3.629469326417878e-05
-8;13;"{'C': 7, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000.0, 'multiclass_strategy': 'ovr'}"
+8;13;"{'C': 7, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000, 'multiclass_strategy': 'ovr'}"
 11;2;"✔"
 11;3;1
 11;4;"Equal to best"
--- a/benchmark/tests/test_files/exreport_excel_STree.test
+++ b/benchmark/tests/test_files/exreport_excel_STree.test
@@ -32,7 +32,7 @@
 7;9;"0.0150468069702512"
 7;10;"0.01404867172241211"
 7;11;"0.002026269126958884"
-7;12;"{'C': 10000.0, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000.0, 'multiclass_strategy': 'ovr'}"
+7;12;"{'C': 10000, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000, 'multiclass_strategy': 'ovr'}"
 8;1;"balloons"
 8;2;"16"
 8;3;"4"
@@ -44,5 +44,5 @@
 8;9;"0.2850146195080759"
 8;10;"0.0008541679382324218"
 8;11;"3.629469326417878e-05"
-8;12;"{'C': 7, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000.0, 'multiclass_strategy': 'ovr'}"
+8;12;"{'C': 7, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000, 'multiclass_strategy': 'ovr'}"
 10;1;"** accuracy compared to STree_default (liblinear-ovr) .:  0.0454"
--- a/benchmark/tests/test_files/report.test
+++ b/benchmark/tests/test_files/report.test
@@ -8,8 +8,8 @@

 Dataset                        Sampl. Feat. Cls Nodes   Leaves  Depth   Score           Time              Hyperparameters 
 ============================== ====== ===== === ======= ======= ======= =============== ================= =============== 
-[96mbalance-scale                     625     4   3    7.00    4.00    3.00 0.970560±0.0150   0.014049±0.0020 {'C': 10000.0, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000.0, 'multiclass_strategy': 'ovr'} 
-[94mballoons                           16     4   2    3.00    2.00    2.00 0.860000±0.2850   0.000854±0.0000 {'C': 7, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000.0, 'multiclass_strategy': 'ovr'} 
+[96mbalance-scale                     625     4   3    7.00    4.00    3.00 0.970560±0.0150   0.014049±0.0020 {'C': 10000, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000, 'multiclass_strategy': 'ovr'} 
+[94mballoons                           16     4   2    3.00    2.00    2.00 0.860000±0.2850   0.000854±0.0000 {'C': 7, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000, 'multiclass_strategy': 'ovr'} 
 [94m*************************************************************************************************************************
 [94m* accuracy compared to STree_default (liblinear-ovr) .:  0.0454                                                         *
 [94m*************************************************************************************************************************
--- a/benchmark/tests/test_files/report_best.test
+++ b/benchmark/tests/test_files/report_best.test
@@ -5,7 +5,7 @@
 Dataset                        Score    File/Message                                                                 Hyperparameters                               
 ============================== ======== ============================================================================ ============================================= 
 balance-scale                  0.980000 results_accuracy_STree_iMac27_2021-10-27_09:40:40_0.json                     {'splitter': 'best', 'max_features': 'auto'} 
-balloons                       0.860000 results_accuracy_STree_iMac27_2021-09-30_11:42:07_0.json                     {'C': 7, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000.0, 'multiclass_strategy': 'ovr'} 
+balloons                       0.860000 results_accuracy_STree_iMac27_2021-09-30_11:42:07_0.json                     {'C': 7, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000, 'multiclass_strategy': 'ovr'} 
 ******************************************************************************************************************************************************************
 * accuracy compared to STree_default (liblinear-ovr) .:  0.0457                                                                                                  *
 ******************************************************************************************************************************************************************
--- a/benchmark/tests/test_files/report_compared.test
+++ b/benchmark/tests/test_files/report_compared.test
@@ -8,8 +8,8 @@

 Dataset                        Sampl. Feat. Cls Nodes   Leaves  Depth   Score           Time              Hyperparameters 
 ============================== ====== ===== === ======= ======= ======= =============== ================= =============== 
-[96mbalance-scale                     625     4   3    7.00    4.00    3.00 0.970560±0.0150   0.014049±0.0020 {'C': 10000.0, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000.0, 'multiclass_strategy': 'ovr'} 
-[94mballoons                           16     4   2    3.00    2.00    2.00 0.860000±0.2850✔  0.000854±0.0000 {'C': 7, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000.0, 'multiclass_strategy': 'ovr'} 
+[96mbalance-scale                     625     4   3    7.00    4.00    3.00 0.970560±0.0150   0.014049±0.0020 {'C': 10000, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000, 'multiclass_strategy': 'ovr'} 
+[94mballoons                           16     4   2    3.00    2.00    2.00 0.860000±0.2850✔  0.000854±0.0000 {'C': 7, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000, 'multiclass_strategy': 'ovr'} 
 [94m*************************************************************************************************************************
 [94m* ✔ Equal to best .....:  1                                                                                             *
 [94m* accuracy compared to STree_default (liblinear-ovr) .:  0.0454                                                         *