COMMENT: correct typos

mathcom · Apr 20, 2022 · 6ace477 · 6ace477
1 parent e57ca8a
commit 6ace477
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Showing 2 changed files with 8 additions and 14 deletions.
diff --git a/2_lococv_on_GDSC.py b/2_lococv_on_GDSC.py
@@ -94,10 +94,7 @@ def main():
         CANCER_outer.append(test_cancertype)
         print('[{}/{}] TEST_CANCER: {}'.format(k+1, n_splits, test_cancertype))
 
-        ## 2-3) Bayesian optimization with gaussian process
-        if verbose > 0:
-            print('[{}/{}] NOW TUNING THE MODEL USING BAYESIAN OPTIMIZATION...'.format(k+1, n_splits))
-
+        ## 2-3) Set the best values of hyperparameters
         BEST_HIDDEN_UNITS = 49
         BEST_LEARNING_RATE_FTRL = 7.94581095185585e-06
         BEST_LEARNING_RATE_ADAM = 0.0004067851789088527
@@ -137,12 +134,12 @@ def main():
                      training_steps=numtrainingsteps,
                      checkpoint_path=checkpoint_path)
 
-        ## 3-6) Fit a model
+        ## 2-6) Fit a model
         history = clf.fit(X_train, S_train, I_train, Y_train,
                           X_valid, S_valid, I_valid, Y_valid,
                           verbose=verbose)
 
-        ## 3-7) Compute the metric
+        ## 2-7) Compute the metric
         Pred_test = clf.predict(X_test, S_test, verbose=verbose)
         Prob_test = clf.predict_proba(X_test, S_test, verbose=verbose)
 
@@ -165,7 +162,7 @@ def main():
             print(timeformat.format(3, datetime.now()))
 
     #######################################################
-    ## 4. Save the results
+    ## 3. Save the results
     ########################################################
     res = pd.DataFrame.from_dict({'CANCERTYPE':CANCER_outer,
                                   'ACCURACY':ACCURACY_outer,

diff --git a/3_lodocv_on_GDSC.py b/3_lodocv_on_GDSC.py
@@ -89,10 +89,7 @@ def main():
         DRUG_outer.append(test_drug)
         print('[{}/{}] TEST_DRUG: {}'.format(k+1, n_splits, test_drug))
 
-        ## 2-3) Bayesian optimization with gaussian process
-        if verbose > 0:
-            print('[{}/{}] NOW TUNING THE MODEL USING BAYESIAN OPTIMIZATION...'.format(k+1, n_splits))
-
+        ## 2-3) Set the best values of hyperparameters
         BEST_HIDDEN_UNITS = 49
         BEST_LEARNING_RATE_FTRL = 7.94581095185585e-06
         BEST_LEARNING_RATE_ADAM = 0.0004067851789088527
@@ -132,12 +129,12 @@ def main():
                      training_steps=numtrainingsteps,
                      checkpoint_path=checkpoint_path)
 
-        ## 3-6) Fit a model
+        ## 2-6) Fit a model
         history = clf.fit(X_train, S_train, I_train, Y_train,
                           X_valid, S_valid, I_valid, Y_valid,
                           verbose=verbose)
 
-        ## 3-7) Compute the metric
+        ## 2-7) Compute the metric
         Pred_test = clf.predict(X_test, S_test, verbose=verbose)
         Prob_test = clf.predict_proba(X_test, S_test, verbose=verbose)
 
@@ -160,7 +157,7 @@ def main():
             print(timeformat.format(3, datetime.now()))
 
     #######################################################
-    ## 4. Save the results
+    ## 3. Save the results
     ########################################################
     res = pd.DataFrame.from_dict({'DRUGNAME':DRUG_outer,
                                   'ACCURACY':ACCURACY_outer,