feat: lazify classification metrics and clean tests

ibis_ml/metrics.py

@@ -18,8 +18,8 @@ def accuracy_score(y_true: dt.Integer, y_pred: dt.Integer) -> float:
 
     Examples
     --------
-    >>> from ibis_ml.metrics import accuracy_score
     >>> import ibis
+    >>> from ibis_ml.metrics import accuracy_score
     >>> ibis.options.interactive = True
     >>> t = ibis.memtable(
     ...     {
@@ -29,9 +29,11 @@ def accuracy_score(y_true: dt.Integer, y_pred: dt.Integer) -> float:
     ...     }
     ... )
     >>> accuracy_score(t.actual, t.prediction)
-    0.5833333333333334
+    ┌──────────┐
+    │ 0.583333 │
+    └──────────┘
     """
-    return (y_true == y_pred).mean().to_pyarrow().as_py()
+    return (y_true == y_pred).mean()  # .to_pyarrow().as_py()
 
 
 def precision_score(y_true: dt.Integer, y_pred: dt.Integer) -> float:
@@ -62,11 +64,13 @@ def precision_score(y_true: dt.Integer, y_pred: dt.Integer) -> float:
     ...     }
     ... )
     >>> precision_score(t.actual, t.prediction)
-    0.6666666666666666
+    ┌──────────┐
+    │ 0.666667 │
+    └──────────┘
     """
     true_positive = (y_true & y_pred).sum()
     predicted_positive = y_pred.sum()
-    return (true_positive / predicted_positive).to_pyarrow().as_py()
+    return true_positive / predicted_positive
 
 
 def recall_score(y_true: dt.Integer, y_pred: dt.Integer) -> float:
@@ -83,6 +87,7 @@ def recall_score(y_true: dt.Integer, y_pred: dt.Integer) -> float:
     -------
     float
         The recall score, representing the fraction of true positive predictions.
+
     Examples
     --------
     >>> from ibis_ml.metrics import recall_score
@@ -96,11 +101,13 @@ def recall_score(y_true: dt.Integer, y_pred: dt.Integer) -> float:
     ...     }
     ... )
     >>> recall_score(t.actual, t.prediction)
-    0.5714285714285714
+    ┌──────────┐
+    │ 0.571429 │
+    └──────────┘
     """
     true_positive = (y_true & y_pred).sum()
     actual_positive = y_true.sum()
-    return (true_positive / actual_positive).to_pyarrow().as_py()
+    return true_positive / actual_positive
 
 
 def f1_score(y_true: dt.Integer, y_pred: dt.Integer) -> float:
@@ -131,7 +138,9 @@ def f1_score(y_true: dt.Integer, y_pred: dt.Integer) -> float:
     ...     }
     ... )
     >>> f1_score(t.actual, t.prediction)
-    0.6153846153846154
+    ┌──────────┐
+    │ 0.615385 │
+    └──────────┘
     """
     precision = precision_score(y_true, y_pred)
     recall = recall_score(y_true, y_pred)

tests/test_metrics.py

@@ -1,11 +1,8 @@
 import ibis
 import pytest
-from sklearn.metrics import accuracy_score as sk_accuracy_score
-from sklearn.metrics import f1_score as sk_f1_score
-from sklearn.metrics import precision_score as sk_precision_score
-from sklearn.metrics import recall_score as sk_recall_score
+import sklearn.metrics
 
-from ibis_ml.metrics import accuracy_score, f1_score, precision_score, recall_score
+import ibis_ml.metrics
 
 
 @pytest.fixture
@@ -19,33 +16,20 @@ def results_table():
     )
 
 
-def test_accuracy_score(results_table):
+@pytest.mark.parametrize(
+    "metric_name",
+    [
+        pytest.param("accuracy_score", id="accuracy_score"),
+        pytest.param("precision_score", id="precision_score"),
+        pytest.param("recall_score", id="recall_score"),
+        pytest.param("f1_score", id="f1_score"),
+    ],
+)
+def test_classification_metrics(results_table, metric_name):
+    ibis_ml_func = getattr(ibis_ml.metrics, metric_name)
+    sklearn_func = getattr(sklearn.metrics, metric_name)
     t = results_table
     df = t.to_pandas()
-    result = accuracy_score(t.actual, t.prediction)
-    expected = sk_accuracy_score(df["actual"], df["prediction"])
-    assert result == pytest.approx(expected, abs=1e-4)
-
-
-def test_precision_score(results_table):
-    t = results_table
-    df = t.to_pandas()
-    result = precision_score(t.actual, t.prediction)
-    expected = sk_precision_score(df["actual"], df["prediction"])
-    assert result == pytest.approx(expected, abs=1e-4)
-
-
-def test_recall_score(results_table):
-    t = results_table
-    df = t.to_pandas()
-    result = recall_score(t.actual, t.prediction)
-    expected = sk_recall_score(df["actual"], df["prediction"])
-    assert result == pytest.approx(expected, abs=1e-4)
-
-
-def test_f1_score(results_table):
-    t = results_table
-    df = t.to_pandas()
-    result = f1_score(t.actual, t.prediction)
-    expected = sk_f1_score(df["actual"], df["prediction"])
+    result = ibis_ml_func(t.actual, t.prediction).to_pyarrow().as_py()
+    expected = sklearn_func(df["actual"], df["prediction"])
     assert result == pytest.approx(expected, abs=1e-4)