training data without x features

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
 lightgbm,0.9849409849409849,0.8469442386692707,0.8185917013944679,0.8023094072140393,0.9084632979829487,0.9922755741127348,0.9946427824048885,0.9962272060364703,0.9883551673944687,0.7016129032258065,0.6425406203840472,0.6083916083916084,0.8285714285714286,87,4753,18,56
+lightgbm_no_x,0.9881969881969882,0.8781216645228386,0.8437523569608822,0.8243364265162629,0.9552017740731065,0.9939482470784641,0.9965686069381093,0.9983232026828757,0.9896114689382921,0.7622950819672131,0.6909361069836553,0.6503496503496503,0.9207920792079208,93,4763,8,50

comparison_lightgbm_no_x.csv

@@ -0,0 +1,2 @@
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lightgbm_tuning_results_no_x.csv

@@ -0,0 +1,193 @@
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+{"learn":[0.03630353367],"iteration":99,"passed_time":3.077618039,"remaining_time":0}
+]}

models/catboost_info/learn_error.tsv

@@ -0,0 +1,101 @@
+iter	Logloss
+0	0.5362182115
+1	0.4350061839
+2	0.3627836859
+3	0.3133898847
+4	0.2751595914
+5	0.2397233684
+6	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
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+36	0.07326399537
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+38	0.0709181573
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+40	0.06887255051
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+47	0.06030088326
+48	0.0594838545
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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
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+10	391	3169
+11	422	3100
+12	452	3028
+13	481	2956
+14	511	2896
+15	540	2838
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+27	904	2326
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+99	3077	0

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}")

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

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__(

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}")

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()