kfcv baseline implemented

quacc/baseline.py

@@ -0,0 +1,14 @@
+
+from statistics import mean
+from typing import Dict
+from sklearn.base import BaseEstimator
+from sklearn.model_selection import cross_validate
+from quapy.data import LabelledCollection
+
+
+def kfcv(c_model: BaseEstimator, train: LabelledCollection) -> Dict:
+    scoring = ["f1_macro"]
+    scores = cross_validate(c_model, train.X, train.y, scoring=scoring)
+    return {
+        "f1_score": mean(scores["test_f1_macro"])
+    }

quacc/dataset.py

@@ -1,4 +1,7 @@
 import quapy as qp
 
-def getImdbTrainTest():
-    return qp.datasets.fetch_reviews("imdb", tfidf=True).train_test
+def get_imdb_traintest():
+    return qp.datasets.fetch_reviews("imdb", tfidf=True).train_test
+
+def get_spambase_traintest():
+    return qp.datasets.fetch_UCIDataset("spambase", verbose=False).train_test

quacc/main.py

@@ -9,7 +9,7 @@ from quacc.estimator import (
     MulticlassAccuracyEstimator,
 )
 
-from quacc.dataset import getImdbTrainTest
+from quacc.dataset import get_imdb_traintest
 
 qp.environ["SAMPLE_SIZE"] = 100
 
@@ -20,7 +20,7 @@ dataset_name = "imdb"
 
 def estimate_multiclass():
     print(dataset_name)
-    train, test = getImdbTrainTest(dataset_name)
+    train, test = get_imdb_traintest(dataset_name)
 
     model = LogisticRegression()
 
@@ -59,7 +59,7 @@ def estimate_multiclass():
 
 def estimate_binary():
     print(dataset_name)
-    train, test = getImdbTrainTest(dataset_name)
+    train, test = get_imdb_traintest(dataset_name)
 
     model = LogisticRegression()
 

tests/test_baseline.py

@@ -0,0 +1,12 @@
+
+from sklearn.linear_model import LogisticRegression
+from quacc.baseline import kfcv
+from quacc.dataset import get_spambase_traintest
+
+
+class TestBaseline:
+
+    def test_kfcv(self):
+        train, _ = get_spambase_traintest()
+        c_model = LogisticRegression()
+        assert "f1_score" in kfcv(c_model, train)

tests/test_dataset.py

@@ -1,32 +1,3 @@
-import pytest
-from quacc.dataset import Rcv1Helper
-
-
-@pytest.fixture
-def rcv1_helper() -> Rcv1Helper:
-    return Rcv1Helper()
-
 
 class TestDataset:
-    def test_rcv1_binary_datasets(self, rcv1_helper):
-        count = 0
-        for X, Y, name in rcv1_helper.rcv1_binary_datasets():
-            count += 1
-            print(X.shape)
-            assert X.shape == (517978, 47236)
-            assert Y.shape == (517978,)
-
-        assert count == 37
-
-    @pytest.mark.parametrize("label", ["CCAT", "GCAT", "M11"])
-    def test_rcv1_binary_dataset_by_label(self, rcv1_helper, label):
-        train, test = rcv1_helper.rcv1_binary_dataset_by_label(label)
-        assert train.X.shape == (23149, 47236)
-        assert train.y.shape == (23149,)
-        assert test.X.shape == (781265, 47236)
-        assert test.y.shape == (781265,)
-
-        assert (
-            dict(rcv1_helper.documents_per_class_rcv1())[label]
-            == train.y.sum() + test.y.sum()
-        )
+    pass