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 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 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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()