From 743db06b295bac692708163349df04b4bc5e4281 Mon Sep 17 00:00:00 2001 From: saeedkhosravi94 Date: Sat, 6 Dec 2025 22:05:19 +0100 Subject: [PATCH] training data without x features --- comparison_catboost_lightgbm.csv | 1 + comparison_lightgbm_no_x.csv | 2 + lightgbm_tuning_results_no_x.csv | 193 ++++++++++++++++++ .../lightgbm_model.cpython-312.pyc | Bin 8672 -> 8679 bytes models/catboost_info/catboost_training.json | 104 ++++++++++ .../catboost_info/learn/events.out.tfevents | Bin 0 -> 5398 bytes models/catboost_info/learn_error.tsv | 101 +++++++++ models/catboost_info/time_left.tsv | 101 +++++++++ models/compare_models.py | 14 +- models/comparison_catboost_lightgbm.csv | 3 + models/lightgbm_model.py | 3 +- test_no_x.py | 42 ++++ training_part_of_data.py | 12 ++ 13 files changed, 568 insertions(+), 8 deletions(-) create mode 100644 comparison_lightgbm_no_x.csv create mode 100644 lightgbm_tuning_results_no_x.csv create mode 100644 models/catboost_info/catboost_training.json create mode 100644 models/catboost_info/learn/events.out.tfevents create mode 100644 models/catboost_info/learn_error.tsv create mode 100644 models/catboost_info/time_left.tsv create mode 100644 models/comparison_catboost_lightgbm.csv create mode 100644 test_no_x.py create mode 100644 training_part_of_data.py diff --git a/comparison_catboost_lightgbm.csv b/comparison_catboost_lightgbm.csv index 02d21f0..b36fa6e 100644 --- a/comparison_catboost_lightgbm.csv +++ b/comparison_catboost_lightgbm.csv @@ -1,3 +1,4 @@ model,accuracy,f1_macro,f2_macro,recall_macro,precision_macro,f1_class0,f2_class0,recall_class0,precision_class0,f1_class1,f2_class1,recall_class1,precision_class1,TP,TN,FP,FN catboost,0.9814814814814815,0.8195693865042805,0.8013174756506312,0.7903526990720451,0.8559205703525894,0.9904901243599122,0.9921698350221925,0.9932928107315029,0.9877032096706961,0.6486486486486487,0.6104651162790697,0.5874125874125874,0.7241379310344828,84,4739,32,59 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+lightgbm,yeo_johnson,class_weight,dart,0.1,100,0.1,0.1,0.8,0.8,10,5,0.9880053953454185,0.8763238649159562,0.8432651737595419,0.8245118929347075,0.949940086104115,0.9938497322377776,0.9963816132484633,0.9980767726035612,0.9896586369846668,0.7587979975941346,0.6901487342706207,0.6509470132658537,0.9102215352235625,105.2,5397.2,10.4,56.4 +lightgbm,yeo_johnson,class_weight,dart,0.1,100,0.1,0.1,0.8,1.0,10,5,0.9884004328913616,0.8807095163898977,0.8476959631018479,0.8289145234692274,0.9537631067371821,0.9940518989065807,0.9965513767258839,0.9982247221167387,0.9899139737723412,0.7673671338732146,0.698840549477812,0.6596043248217162,0.917612239702023,106.6,5398.0,9.6,55.0 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0.2113156522 +7 0.1953507088 +8 0.1840143441 +9 0.1706949477 +10 0.1620772533 +11 0.1545441635 +12 0.1465173236 +13 0.1415301698 +14 0.1355367956 +15 0.1308604636 +16 0.1258709194 +17 0.121327471 +18 0.1173725037 +19 0.1136409199 +20 0.1097959508 +21 0.1067253666 +22 0.1024064669 +23 0.09901992217 +24 0.09567527484 +25 0.09377244268 +26 0.09156823556 +27 0.08858936107 +28 0.08620594774 +29 0.08449640594 +30 0.08268042091 +31 0.08113275342 +32 0.07948416585 +33 0.07752156705 +34 0.0758993004 +35 0.07462952526 +36 0.07326399537 +37 0.07204226808 +38 0.0709181573 +39 0.0697872959 +40 0.06887255051 +41 0.06779376451 +42 0.06655209373 +43 0.06472935248 +44 0.06358214688 +45 0.06235626673 +46 0.06135828091 +47 0.06030088326 +48 0.0594838545 +49 0.05857401953 +50 0.05779726387 +51 0.05705401197 +52 0.0563860381 +53 0.05578116129 +54 0.05501245032 +55 0.05448421539 +56 0.05396827749 +57 0.05365568996 +58 0.05285317024 +59 0.05220569231 +60 0.05189669425 +61 0.05100777067 +62 0.05061405067 +63 0.0500145832 +64 0.04913773926 +65 0.04856317666 +66 0.04793897559 +67 0.04735074265 +68 0.04681185043 +69 0.04639291477 +70 0.04587299669 +71 0.04538571251 +72 0.04524987562 +73 0.04504062268 +74 0.04475003947 +75 0.04418860638 +76 0.04384235707 +77 0.04345384668 +78 0.0429983499 +79 0.04232998964 +80 0.04149603099 +81 0.04131352094 +82 0.04087094967 +83 0.0407229616 +84 0.04048230194 +85 0.04019292818 +86 0.03999431065 +87 0.03963063313 +88 0.03950575155 +89 0.03937511809 +90 0.03902639674 +91 0.0386153965 +92 0.03849577538 +93 0.03828038492 +94 0.037581457 +95 0.03730521765 +96 0.03698274753 +97 0.03692042628 +98 0.03669428709 +99 0.03630353367 diff --git a/models/catboost_info/time_left.tsv b/models/catboost_info/time_left.tsv new file mode 100644 index 0000000..7f30411 --- /dev/null +++ b/models/catboost_info/time_left.tsv @@ -0,0 +1,101 @@ +iter Passed Remaining +0 37 3689 +1 70 3472 +2 106 3454 +3 146 3518 +4 213 4064 +5 243 3818 +6 272 3614 +7 300 3459 +8 333 3374 +9 363 3273 +10 391 3169 +11 422 3100 +12 452 3028 +13 481 2956 +14 511 2896 +15 540 2838 +16 570 2787 +17 602 2743 +18 631 2692 +19 663 2652 +20 692 2605 +21 722 2562 +22 755 2529 +23 785 2487 +24 816 2448 +25 845 2407 +26 874 2363 +27 904 2326 +28 938 2298 +29 970 2264 +30 1000 2226 +31 1027 2182 +32 1055 2143 +33 1083 2103 +34 1110 2062 +35 1140 2027 +36 1171 1994 +37 1202 1962 +38 1236 1933 +39 1272 1908 +40 1305 1878 +41 1340 1850 +42 1367 1812 +43 1396 1777 +44 1425 1741 +45 1454 1707 +46 1484 1674 +47 1517 1643 +48 1547 1610 +49 1575 1575 +50 1605 1542 +51 1637 1511 +52 1668 1479 +53 1698 1447 +54 1730 1416 +55 1760 1383 +56 1790 1350 +57 1816 1315 +58 1843 1281 +59 1873 1248 +60 1900 1214 +61 1929 1182 +62 1958 1150 +63 1988 1118 +64 2019 1087 +65 2049 1055 +66 2076 1022 +67 2104 990 +68 2131 957 +69 2160 925 +70 2189 894 +71 2218 862 +72 2247 831 +73 2279 800 +74 2309 769 +75 2338 738 +76 2364 706 +77 2394 675 +78 2424 644 +79 2454 613 +80 2483 582 +81 2511 551 +82 2541 520 +83 2570 489 +84 2598 458 +85 2628 427 +86 2654 396 +87 2681 365 +88 2712 335 +89 2739 304 +90 2772 274 +91 2801 243 +92 2828 212 +93 2863 182 +94 2918 153 +95 2962 123 +96 2993 92 +97 3018 61 +98 3046 30 +99 3077 0 diff --git a/models/compare_models.py b/models/compare_models.py index 5d1142a..8cec39d 100644 --- a/models/compare_models.py +++ b/models/compare_models.py @@ -4,14 +4,14 @@ from lightgbm_model import LIGHT_GBM data_frame = pandas.read_csv("../data/Ketamine_icp_no_missing.csv") -cat_boost_results = pandas.read_csv("./cat_boost_tuning_results_no_x.csv") -lgbm_results = pandas.read_csv("./lightgbm_tuning_results.csv") +cat_boost_results = pandas.read_csv("../cat_boost_tuning_results.csv") +lgbm_results = pandas.read_csv("../lightgbm_tuning_results.csv") -def get_best_params(data_frame, metrics=["f2_class1", "f1_class1"]): - max_f2 = cat_boost_results[metrics[0]].max() +def get_best_params(results, metrics=["f2_class1", "f1_class1"]): + max_f2 = results[metrics[0]].max() - best_f2_rows = cat_boost_results[cat_boost_results[metrics[0]] == max_f2] + best_f2_rows = results[results[metrics[0]] == max_f2] best_row = best_f2_rows.loc[best_f2_rows[metrics[1]].idxmax()] @@ -44,12 +44,12 @@ lgbm_test_metrics_clean = clean_metrics(lgbm_test_metrics) comparison_df = pd.DataFrame( [ - {"model": "lightgbm_no_x", **cat_test_metrics_clean}, + {"model": "catboost", **cat_test_metrics_clean}, {"model": "lightgbm", **lgbm_test_metrics_clean}, ] ) -comparison_filename = "comparison_lightgbm_no_x_vs_lightgbm.csv" +comparison_filename = "comparison_catboost_lightgbm.csv" comparison_df.to_csv(comparison_filename, index=False) print(f"Comparison saved to: {comparison_filename}") diff --git a/models/comparison_catboost_lightgbm.csv b/models/comparison_catboost_lightgbm.csv new file mode 100644 index 0000000..1f2a9c2 --- /dev/null +++ b/models/comparison_catboost_lightgbm.csv @@ -0,0 +1,3 @@ +model,accuracy,f1_macro,f2_macro,recall_macro,precision_macro,f1_class0,f2_class0,recall_class0,precision_class0,f1_class1,f2_class1,recall_class1,precision_class1,TP,TN,FP,FN +catboost,0.9814814814814815,0.8195693865042805,0.8013174756506312,0.7903526990720451,0.8559205703525894,0.9904901243599122,0.9921698350221925,0.9932928107315029,0.9877032096706961,0.6486486486486487,0.6104651162790697,0.5874125874125874,0.7241379310344828,84,4739,32,59 +lightgbm,0.9824989824989825,0.8313827758297916,0.814631097485035,0.8044435128903794,0.8640127583880268,0.9910098264687435,0.9925038736965535,0.9935024103961434,0.9885297184567258,0.6717557251908397,0.6367583212735166,0.6153846153846154,0.7394957983193278,88,4740,31,55 diff --git a/models/lightgbm_model.py b/models/lightgbm_model.py index 0d27a8a..d60f092 100644 --- a/models/lightgbm_model.py +++ b/models/lightgbm_model.py @@ -5,10 +5,11 @@ from tabnanny import verbose import lightgbm as lgb import pandas from imblearn.over_sampling import KMeansSMOTE -from model_utils import average_fold_results, get_metrics, scaling_handler from sklearn.model_selection import StratifiedKFold, train_test_split from tqdm import tqdm +from models.model_utils import average_fold_results, get_metrics, scaling_handler + class LIGHT_GBM: def __init__( diff --git a/test_no_x.py b/test_no_x.py new file mode 100644 index 0000000..d397a05 --- /dev/null +++ b/test_no_x.py @@ -0,0 +1,42 @@ +import pandas + +from models.lightgbm_model import LIGHT_GBM + +data_frame = pandas.read_csv("./data/Ketamine_icp_no_x.csv") + +lgbm_results = pandas.read_csv("./lightgbm_tuning_results_no_x.csv") + + +def get_best_params(results, metrics=["f2_class1", "f1_class1"]): + max_f2 = results[metrics[0]].max() + + best_f2_rows = results[results[metrics[0]] == max_f2] + + best_row = best_f2_rows.loc[best_f2_rows[metrics[1]].idxmax()] + + return best_row.to_dict() + + +lgbm_best_params = get_best_params(lgbm_results) +lgbm_model = LIGHT_GBM(data_frame, params=lgbm_best_params) +lgbm_model.fit() +lgbm_test_metrics = lgbm_model.eval() +print(lgbm_test_metrics) + + +def clean_metrics(metrics): + return {k: float(v) if hasattr(v, "item") else v for k, v in metrics.items()} + + +lgbm_test_metrics_clean = clean_metrics(lgbm_test_metrics) + +comparison_df = pandas.DataFrame( + [ + {"model": "lightgbm", **lgbm_test_metrics_clean}, + ] +) + +comparison_filename = "comparison_catboost_lightgbm_no_x.csv" +comparison_df.to_csv(comparison_filename, index=False) + +print(f"Comparison saved to: {comparison_filename}") diff --git a/training_part_of_data.py b/training_part_of_data.py new file mode 100644 index 0000000..559cf62 --- /dev/null +++ b/training_part_of_data.py @@ -0,0 +1,12 @@ +import pandas + +from models.lightgbm_model import LIGHT_GBM + +data_frame = pandas.read_csv("../data/Ketamine_icp_no_x.csv") + +model = LIGHT_GBM( + data_frame, + output_file_tuning="lightgbm_tuning_results_no_x.csv", +) + +results = model.tune()