1. Run experiments for article of nonparametric modification

iuliivasilev · Jun 21, 2024 · 053100a · 053100a
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diff --git a/tests/PARAMS/SCHEME_PARAM.py b/tests/PARAMS/SCHEME_PARAM.py
@@ -4,7 +4,7 @@
     "criterion": ["peto", "tarone-ware", "wilcoxon", "logrank"],  # "ibswei", "maxcombo",
     "min_samples_leaf": [0.05, 0.01, 0.001],
     "l_reg": [0, 0.01, 0.1, 0.5, 0.9],  # [0, 0.001, 0.01, 0.1, 0.2]
-    "leaf_model": ["base_zero_after"],
+    "leaf_model": ["base_meaning"],  # , "base_zero_after"
     'cut': [False],  # True,
     "woe": [True],
     "signif": [0.05, 0.1, 1.0],
@@ -16,7 +16,7 @@
     "size_sample": [0.3, 0.5, 0.7, 1.0],  # 0.7
     "n_estimators": [50],
     "depth": [7],  # 15
-    "l_reg": [0, 0.01, 0.1, 0.5, 0.9],
+    "l_reg": [0],  # 0.01, 0.1, 0.5, 0.9],
     "ens_metric_name": ["IBS_REMAIN"],  # ["bic", "roc", "ibs"], "IBS_WW", "IBS_REMAIN"
     "criterion": ["peto", "tarone-ware", "wilcoxon", "logrank"],  # "ibswei",
     "leaf_model": ["base_zero_after"],  # ["base_fast", "base_zero_after"]
@@ -87,7 +87,7 @@
     "size_sample": [0.5, 1.0],  # 0.7
     "n_estimators": [50],
     "depth": [7],  # 10
-    "l_reg": [0, 0.01, 0.1, 0.5, 0.9],
+    "l_reg": [0],  # 0.01, 0.1, 0.5, 0.9],
     "ens_metric_name": ["IBS"],
     "criterion": ["peto", "tarone-ware", "wilcoxon", "logrank"],  # "symm_peto",
     "leaf_model": ["base_zero_after"],  # "base_fast"

diff --git a/tests/test_experiments.py b/tests/test_experiments.py
@@ -245,7 +245,7 @@ def dir_path():
 # )
 
 @pytest.mark.parametrize(
-    "best_metric", ["LOGLIKELIHOOD"]  # ["IBS_REMAIN"], ["CI", "CI_CENS", "LOGLIKELIHOOD", "IBS", "IBS_WW", "IBS_REMAIN", "IAUC_WW_TI", "AUPRC"]
+    "best_metric", ["IBS_REMAIN"]  # ["IBS_REMAIN"], ["CI", "CI_CENS", "LOGLIKELIHOOD", "IBS", "IBS_WW", "IBS_REMAIN", "IAUC_WW_TI", "AUPRC"]
 )
 # @pytest.mark.parametrize(
 #     "mode_wei", ["exp", "sigmoid", "linear"]  # "exp", "sigmoid"
@@ -263,25 +263,25 @@ def test_dataset_exp(dir_path, dataset, best_metric, bins_sch="origin", mode="CV
 
     # prefix = f"{best_metric}_STRATTIME+_PARBSTR_test_wide_{bins_sch}"
 
-    # prefix = f"{best_metric}_STRATTIME+_EXT10_EQ_REG_TREE_ALL_BINS_{bins_sch}"
+    prefix = f"{best_metric}_STRATTIME+_EXT10_MEAN_EQ_REG_TREE_ALL_BINS_{bins_sch}"
     # prefix = f"{best_metric}_STRATTIME+_EXT10_NORMAL_EQ_REG_TREE_ALL_BINS_{bins_sch}"
 
     # prefix = f"{best_metric}_PARBSTR"
     # prefix = f"{best_metric}_STRATTIME+_EXT10_NORMAL_EQ_REG_PAR_BSTR_ALL_BINS_{bins_sch}"
     # prefix = f"{best_metric}_STRATTIME+_EXT10_NORMAL_EQ_REG_CLEVERBOOST_ALL_BINS_{bins_sch}"
     # prefix = f"{best_metric}_STRATTIME+_EXT10_NORMAL_EQ_REG_{mode_wei}_reg(0_01)_PART_BOOST_ALL_BINS_{bins_sch}"
 
-    prefix = f"{best_metric}_STRATTIME+_scsurv_extended_no_scale"  # "scsurv", "bstr_full_WB", SHORT_CNT_DIFF_
-    res_exp = run(dataset, with_self=[], with_external=True, mode=mode,
-                  # dir_path=dir_path+"\\",
-                  bins_sch=bins_sch, best_metric=best_metric)  # Only scikit-survival
-
-    # storage_path = os.path.join("D:", os.sep, "Vasilev", "SA", dataset)
-    # if not os.path.exists(storage_path):
-    #     os.makedirs(storage_path)
-    # res_exp = run(dataset, with_self=["PARBSTR"], with_external=False, mode=mode,  # CLEVERBOOST
-    #               #  dir_path=storage_path+"\\",
-    #               bins_sch=bins_sch, best_metric=best_metric, mode_wei=mode_wei)  # ["TREE", "BSTR", "BOOST"]
+    # prefix = f"{best_metric}_STRATTIME+_scsurv_extended_no_scale"  # "scsurv", "bstr_full_WB", SHORT_CNT_DIFF_
+    # res_exp = run(dataset, with_self=[], with_external=True, mode=mode,
+    #               # dir_path=dir_path+"\\",
+    #               bins_sch=bins_sch, best_metric=best_metric)  # Only scikit-survival
+
+    storage_path = os.path.join("D:", os.sep, "Vasilev", "SA", dataset)
+    if not os.path.exists(storage_path):
+        os.makedirs(storage_path)
+    res_exp = run(dataset, with_self=["TREE"], with_external=False, mode=mode,  # CLEVERBOOST
+                  #  dir_path=storage_path+"\\",
+                  bins_sch=bins_sch, best_metric=best_metric, mode_wei=mode_wei)  # ["TREE", "PARBSTR", "BSTR", "BOOST"]
 
     df_full = res_exp.get_result()
     df_criterion = res_exp.get_best_by_mode(stratify="criterion")