From 8650291f66ba068fad7aae63e270d8b1fd444f72 Mon Sep 17 00:00:00 2001
From: Long Nguyen <lucius.nguyen@unb.ca>
Date: Sun, 8 Dec 2024 10:27:24 -0800
Subject: [PATCH] fix table output

---
 results/models/disease_pipeline.pickle | Bin 4440 -> 4440 bytes
 results/tables/cross_val_score.csv     |  23 ++++++++++++-----------
 results/tables/cross_val_std.csv       |  23 ++++++++++++-----------
 scripts/4_training_models.py           |  10 +++++++---
 4 files changed, 31 insertions(+), 25 deletions(-)

diff --git a/results/models/disease_pipeline.pickle b/results/models/disease_pipeline.pickle
index 4a9ca8b1bc06d001c048718c54a65bfd2b9a9679..ebec341fd11f25637388ad1bc2f8702a171c8144 100644
GIT binary patch
delta 409
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DjJ~IA

delta 410
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BrtSa$

diff --git a/results/tables/cross_val_score.csv b/results/tables/cross_val_score.csv
index 98c9b5c..c734b28 100644
--- a/results/tables/cross_val_score.csv
+++ b/results/tables/cross_val_score.csv
@@ -1,11 +1,12 @@
-dummy,logreg,svc,logreg_bal,svc_bal
-0.001,0.009,0.007,0.009,0.008
-0.003,0.006,0.006,0.005,0.006
-0.746,0.797,0.818,0.731,0.747
-0.746,0.85,0.896,0.822,0.916
-0.0,0.638,0.824,0.477,0.524
-0.0,0.789,0.969,0.609,0.779
-0.0,0.44,0.38,0.68,0.62
-0.0,0.56,0.61,0.84,0.94
-0.0,0.517,0.512,0.555,0.563
-0.0,0.655,0.747,0.706,0.851
+index,dummy,dummy,logreg,logreg,svc,svc,logreg_bal,logreg_bal,svc_bal,svc_bal
+,mean,std,mean,std,mean,std,mean,std,mean,std
+fit_time,0.001,0.0,0.009,0.0,0.007,0.0,0.009,0.0,0.008,0.001
+score_time,0.003,0.0,0.006,0.0,0.006,0.001,0.006,0.0,0.006,0.0
+test_accuracy,0.746,0.004,0.797,0.063,0.818,0.036,0.731,0.035,0.747,0.099
+train_accuracy,0.746,0.001,0.85,0.012,0.896,0.015,0.822,0.02,0.916,0.01
+test_precision,0.0,0.0,0.638,0.159,0.824,0.182,0.477,0.056,0.524,0.154
+train_precision,0.0,0.0,0.789,0.032,0.969,0.018,0.609,0.031,0.779,0.034
+test_recall,0.0,0.0,0.44,0.167,0.38,0.084,0.68,0.179,0.62,0.11
+train_recall,0.0,0.0,0.56,0.038,0.61,0.065,0.84,0.038,0.94,0.022
+test_f1,0.0,0.0,0.517,0.167,0.512,0.094,0.555,0.09,0.563,0.13
+train_f1,0.0,0.0,0.655,0.033,0.747,0.047,0.706,0.031,0.851,0.014
diff --git a/results/tables/cross_val_std.csv b/results/tables/cross_val_std.csv
index 5b7da1b..c734b28 100644
--- a/results/tables/cross_val_std.csv
+++ b/results/tables/cross_val_std.csv
@@ -1,11 +1,12 @@
-dummy,logreg,svc,logreg_bal,svc_bal
-0.0,0.001,0.0,0.0,0.001
-0.001,0.001,0.0,0.0,0.0
-0.004,0.063,0.036,0.035,0.099
-0.001,0.012,0.015,0.02,0.01
-0.0,0.159,0.182,0.056,0.154
-0.0,0.032,0.018,0.031,0.034
-0.0,0.167,0.084,0.179,0.11
-0.0,0.038,0.065,0.038,0.022
-0.0,0.167,0.094,0.09,0.13
-0.0,0.033,0.047,0.031,0.014
+index,dummy,dummy,logreg,logreg,svc,svc,logreg_bal,logreg_bal,svc_bal,svc_bal
+,mean,std,mean,std,mean,std,mean,std,mean,std
+fit_time,0.001,0.0,0.009,0.0,0.007,0.0,0.009,0.0,0.008,0.001
+score_time,0.003,0.0,0.006,0.0,0.006,0.001,0.006,0.0,0.006,0.0
+test_accuracy,0.746,0.004,0.797,0.063,0.818,0.036,0.731,0.035,0.747,0.099
+train_accuracy,0.746,0.001,0.85,0.012,0.896,0.015,0.822,0.02,0.916,0.01
+test_precision,0.0,0.0,0.638,0.159,0.824,0.182,0.477,0.056,0.524,0.154
+train_precision,0.0,0.0,0.789,0.032,0.969,0.018,0.609,0.031,0.779,0.034
+test_recall,0.0,0.0,0.44,0.167,0.38,0.084,0.68,0.179,0.62,0.11
+train_recall,0.0,0.0,0.56,0.038,0.61,0.065,0.84,0.038,0.94,0.022
+test_f1,0.0,0.0,0.517,0.167,0.512,0.094,0.555,0.09,0.563,0.13
+train_f1,0.0,0.0,0.655,0.033,0.747,0.047,0.706,0.031,0.851,0.014
diff --git a/scripts/4_training_models.py b/scripts/4_training_models.py
index 6645110..eb093b1 100644
--- a/scripts/4_training_models.py
+++ b/scripts/4_training_models.py
@@ -90,11 +90,15 @@ def main(train, write_to):
                            return_train_score=True)
         ).agg(['mean', 'std']).round(3).T
     
-    # Save cross-validation results
-    pd.concat(cross_val_results, axis='columns').xs('std', axis='columns', level=1).to_csv(
+    # Save cross-validation results (standard deviation)
+    std_df = pd.concat(cross_val_results, axis='columns').reset_index()
+    std_df.to_csv(
         os.path.join(write_to, "tables", "cross_val_std.csv"), index=False
     )
-    pd.concat(cross_val_results, axis='columns').xs('mean', axis='columns', level=1).to_csv(
+    
+    # Save cross-validation results (mean)
+    mean_df = pd.concat(cross_val_results, axis='columns').reset_index()
+    mean_df.to_csv(
         os.path.join(write_to, "tables", "cross_val_score.csv"), index=False
     )