Fixes #812 (#813)

* Fixes #812

* [pre-commit.ci] auto fixes from pre-commit.com hooks

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* named arguments in log_sklearn_plot

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* replaced sklearn_kwargs with kwargs

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

src/dvclive/live.py

@@ -624,6 +624,10 @@ def log_sklearn_plot(
         labels: Union[List, np.ndarray],
         predictions: Union[List, Tuple, np.ndarray],
         name: Optional[str] = None,
+        title: Optional[str] = None,
+        x_label: Optional[str] = None,
+        y_label: Optional[str] = None,
+        normalized: Optional[bool] = None,
         **kwargs,
     ):
         """
@@ -638,14 +642,17 @@ def log_sklearn_plot(
                 "roc"): a supported plot type.
             labels (List | np.ndarray): array of ground truth labels.
             predictions (List | np.ndarray): array of predicted labels (for
-            `"confusion_matrix"`) or predicted probabilities (for other plots).
+                `"confusion_matrix"`) or predicted probabilities (for other plots).
             name (str): optional name of the output file. If not provided, `kind` will
-            be used as name.
+                be used as name.
+            title (str): optional title to be displayed.
+            x_label (str): optional label for the x axis.
+            y_label (str): optional label for the y axis.
+            normalized (bool): optional, `confusion_matrix` with values normalized to
+                `<0, 1>` range.
             kwargs: additional arguments to tune the result. Arguments are passed to the
                 scikit-learn function (e.g. `drop_intermediate=True` for the `"roc"`
-                type). Plus extra arguments supported by the type of a plot are:
-                 - `normalized`: default to `False`. `confusion_matrix` with values
-                    normalized to `<0, 1>` range.
+                type).
         Raises:
             InvalidPlotTypeError: thrown if the provided `kind` does not correspond to
                 any of the supported plots.
@@ -654,9 +661,15 @@ def log_sklearn_plot(
 
         plot_config = {
             k: v
-            for k, v in kwargs.items()
-            if k in ("title", "x_label", "y_label", "normalized")
+            for k, v in {
+                "title": title,
+                "x_label": x_label,
+                "y_label": y_label,
+                "normalized": normalized,
+            }.items()
+            if v is not None
         }
+
         name = name or kind
         if name in self._plots:
             plot = self._plots[name]
@@ -666,11 +679,8 @@ def log_sklearn_plot(
         else:
             raise InvalidPlotTypeError(name)
 
-        sklearn_kwargs = {
-            k: v for k, v in kwargs.items() if k not in plot_config or k != "normalized"
-        }
         plot.step = self.step
-        plot.dump(val, **sklearn_kwargs)
+        plot.dump(val, **kwargs)
         logger.debug(f"Logged {name}")
 
     def _read_params(self):

tests/plots/test_sklearn.py

@@ -162,7 +162,7 @@ def test_custom_title(tmp_dir, y_true_y_pred_y_score):
     live = Live()
     out = tmp_dir / live.plots_dir / SKLearnPlot.subfolder
 
-    y_true, y_pred, _ = y_true_y_pred_y_score
+    y_true, y_pred, y_score = y_true_y_pred_y_score
 
     live.log_sklearn_plot(
         "confusion_matrix",
@@ -174,8 +174,38 @@ def test_custom_title(tmp_dir, y_true_y_pred_y_score):
     live.log_sklearn_plot(
         "confusion_matrix", y_true, y_pred, name="val/cm", title="Val Confusion Matrix"
     )
+    live.log_sklearn_plot(
+        "precision_recall",
+        y_true,
+        y_score,
+        name="val/prc",
+        title="Val Precision Recall",
+    )
     assert (out / "train" / "cm.json").exists()
     assert (out / "val" / "cm.json").exists()
+    assert (out / "val" / "prc.json").exists()
 
     assert live._plots["train/cm"].plot_config["title"] == "Train Confusion Matrix"
     assert live._plots["val/cm"].plot_config["title"] == "Val Confusion Matrix"
+    assert live._plots["val/prc"].plot_config["title"] == "Val Precision Recall"
+
+
+def test_custom_labels(tmp_dir, y_true_y_pred_y_score):
+    """https://github.com/iterative/dvclive/issues/453"""
+    live = Live()
+    out = tmp_dir / live.plots_dir / SKLearnPlot.subfolder
+
+    y_true, _, y_score = y_true_y_pred_y_score
+
+    live.log_sklearn_plot(
+        "precision_recall",
+        y_true,
+        y_score,
+        name="val/prc",
+        x_label="x_test",
+        y_label="y_test",
+    )
+    assert (out / "val" / "prc.json").exists()
+
+    assert live._plots["val/prc"].plot_config["x_label"] == "x_test"
+    assert live._plots["val/prc"].plot_config["y_label"] == "y_test"