main updated

quacc/main.py

@@ -2,7 +2,7 @@ import pandas as pd
 import quapy as qp
 from quapy.method.aggregative import SLD
 from quapy.protocol import APP
-from sklearn.linear_model import LogisticRegression
+from sklearn.svm import SVC
 
 import quacc.evaluation as eval
 from quacc.estimator import AccuracyEstimator
@@ -17,23 +17,24 @@ pd.set_option("display.float_format", "{:.4f}".format)
 def test_2(dataset_name):
     train, test = get_dataset(dataset_name)
 
-    model = LogisticRegression()
+    model = SVC(probability=True)
 
-    print(f"fitting model {model.__class__.__name__}...", end=" ")
+    print(f"fitting model {model.__class__.__name__}...", end=" ", flush=True)
     model.fit(*train.Xy)
     print("fit")
 
-    qmodel = SLD(LogisticRegression())
+    qmodel = SLD(SVC(probability=True))
     estimator = AccuracyEstimator(model, qmodel)
 
-    print(f"fitting qmodel {qmodel.__class__.__name__}...", end=" ")
+    print(f"fitting qmodel {qmodel.__class__.__name__}...", end=" ", flush=True)
     estimator.fit(train)
     print("fit")
 
     n_prevalences = 21
     repreats = 1000
     protocol = APP(test, n_prevalences=n_prevalences, repeats=repreats)
-    print( f"Tests:\n\
+    print(
+        f"Tests:\n\
         protocol={protocol.__class__.__name__}\n\
         n_prevalences={n_prevalences}\n\
         repreats={repreats}\n\
@@ -49,9 +50,9 @@ def test_2(dataset_name):
 
 def main():
     for dataset_name in [
-        "hp",
         "imdb",
-        "spambase",
+        # "hp",
+        # "spambase",
     ]:
         print(dataset_name)
         test_2(dataset_name)