setup old-defaults study

asreview · Jan 29, 2025 · dfe529d · dfe529d
1 parent 2465ff7
commit dfe529d
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Showing 3 changed files with 14 additions and 13 deletions.
diff --git a/asreview2-optuna/classifiers.py b/asreview2-optuna/classifiers.py
@@ -9,7 +9,7 @@
 
 def naive_bayes_params(trial: optuna.trial.FrozenTrial):
     # Use logarithmic normal distribution for alpha (alpha effect is non-linear)
-    alpha = trial.suggest_float("alpha", 0.5, 50, log=True)
+    alpha = 3.822 #trial.suggest_float("alpha", 0.5, 50, log=True)
     #alpha = trial.suggest_float("nb__alpha", 1.0, 15.0)
     return {"alpha": alpha}
 

diff --git a/asreview2-optuna/feature_extractors.py b/asreview2-optuna/feature_extractors.py
@@ -5,21 +5,22 @@
 def tfidf_params(trial: optuna.trial.FrozenTrial):
     #max_features = trial.suggest_int("tfidf__max_features", 15_000, 50_000)
 
-    max_df = trial.suggest_float("tfidf__max_df", 0.7, 1.0)
+    #max_df = trial.suggest_float("tfidf__max_df", 0.7, 1.0)
 
-    min_df = trial.suggest_int("tfidf__min_df", 2, 4)
+    #min_df = trial.suggest_int("tfidf__min_df", 2, 4)
 
     #max_ngram_range = trial.suggest_int("tfidf__max_ngram_range", 1, 3)
-    ngram_range = (1, 2)
+    #ngram_range = (1, 2)
 
-    sublinear_tf = True#trial.suggest_categorical("tfidf__sublinear_tf", [True, False])
+    #sublinear_tf = True#trial.suggest_categorical("tfidf__sublinear_tf", [True, False])
 
     return {
         #"max_features": max_features,
-        "max_df": max_df,
-        "min_df": min_df,
-        "ngram_range": ngram_range,
-        "sublinear_tf": sublinear_tf,
+        #"max_df": max_df,
+        #"min_df": min_df,
+        #"ngram_range": ngram_range,
+        #sublinear_tf": sublinear_tf,
+        #"ngram_range": (1, 1),
     }
 
 

diff --git a/asreview2-optuna/main.py b/asreview2-optuna/main.py
@@ -19,12 +19,12 @@
 from feature_extractors import feature_extractor_params, feature_extractors
 
 # Study variables
-VERSION = 1
+VERSION = 2
 STUDY_SET = "full"
 CLASSIFIER_TYPE = "nb"  # Options: "nb", "log", "svm", "rf"
-FEATURE_EXTRACTOR_TYPE = "labse"  # Options: "tfidf", "onehot", "labse", "bge-m3"
+FEATURE_EXTRACTOR_TYPE = "tfidf"  # Options: "tfidf", "onehot", "labse", "bge-m3"
 PICKLE_FOLDER_PATH = Path("synergy-dataset", f"pickles_{FEATURE_EXTRACTOR_TYPE}")
-PRE_PROCESSED_FMS = True  # False = on the fly
+PRE_PROCESSED_FMS = False  # False = on the fly
 PARALLELIZE_OBJECTIVE = True
 
 # Optuna variables
@@ -164,7 +164,7 @@ def process_row(row, clf_params, fe_params, ratio):
 def objective_report(report_order):
     def objective(trial):
         # Use normal distribution for ratio (ratio effect is linear)
-        ratio = trial.suggest_float("ratio", 1.0, 2.0)
+        ratio = trial.suggest_float("ratio", 1.0, 10.0)
         # ratio = 1.5
         clf_params = classifier_params[CLASSIFIER_TYPE](trial)
         fe_params = (