Add metrics ROC AUC, f1, precision, recall.

src/evidently/metrics/classification_performance/quality_by_class_metric.py

@@ -96,6 +96,8 @@ def calculate(self, data: InputData) -> ClassificationQualityByClassResult:
             current_roc_aucs = sklearn.metrics.roc_auc_score(
                 binaraized_target, prediction.prediction_probas, average=None
             ).tolist()
+            for idx, item in enumerate(list(prediction.prediction_probas.columns)):
+                metrics_matrix[item].roc_auc = current_roc_aucs[idx]
         reference_roc_aucs = None
 
         reference = None
@@ -115,6 +117,8 @@ def calculate(self, data: InputData) -> ClassificationQualityByClassResult:
                 reference_roc_aucs = sklearn.metrics.roc_auc_score(
                     binaraized_target, ref_prediction.prediction_probas, average=None
                 ).tolist()
+                for idx, item in enumerate(list(prediction.prediction_probas.columns)):
+                    ref_metrics[item].roc_auc = reference_roc_aucs[idx]
             reference = ClassificationQuality(metrics=ref_metrics, roc_aucs=reference_roc_aucs)
         return ClassificationQualityByClassResult(
             columns=columns,

src/evidently/v2/metrics/data_quality.py

@@ -0,0 +1,86 @@
+from typing import Optional
+
+from evidently.metrics import ClassificationQualityByClass
+from evidently.v2.datasets import Dataset
+from evidently.v2.metrics import ByLabelValue
+from evidently.v2.metrics import Metric
+from evidently.v2.metrics.base import TResult
+from evidently.v2.report import Context
+
+
+class F1Metric(Metric[ByLabelValue]):
+    def __init__(self, probas_threshold: Optional[float] = None, k: Optional[int] = None):
+        super().__init__("f1")
+        self.probas_threshold = probas_threshold
+        self.k = k
+
+    def calculate(self, current_data: Dataset, reference_data: Optional[Dataset]) -> TResult:
+        raise ValueError()
+
+    def _call(self, context: Context) -> ByLabelValue:
+        result = context.get_legacy_metric(ClassificationQualityByClass(self.probas_threshold, self.k))
+        return ByLabelValue(
+            {k: v.f1 for k, v in result.current.metrics.items()},
+        )
+
+    def display_name(self) -> str:
+        return "F1 metric"
+
+
+class PrecisionMetric(Metric[ByLabelValue]):
+    def __init__(self, probas_threshold: Optional[float] = None, k: Optional[int] = None):
+        super().__init__("precision")
+        self.probas_threshold = probas_threshold
+        self.k = k
+
+    def calculate(self, current_data: Dataset, reference_data: Optional[Dataset]) -> TResult:
+        raise ValueError()
+
+    def _call(self, context: Context) -> ByLabelValue:
+        result = context.get_legacy_metric(ClassificationQualityByClass(self.probas_threshold, self.k))
+        return ByLabelValue(
+            {k: v.precision for k, v in result.current.metrics.items()},
+        )
+
+    def display_name(self) -> str:
+        return "Precision metric"
+
+
+class RecallMetric(Metric[ByLabelValue]):
+    def __init__(self, probas_threshold: Optional[float] = None, k: Optional[int] = None):
+        super().__init__("recall")
+        self.probas_threshold = probas_threshold
+        self.k = k
+
+    def calculate(self, current_data: Dataset, reference_data: Optional[Dataset]) -> TResult:
+        raise ValueError()
+
+    def _call(self, context: Context) -> ByLabelValue:
+        result = context.get_legacy_metric(ClassificationQualityByClass(self.probas_threshold, self.k))
+
+        return ByLabelValue(
+            {k: v.recall for k, v in result.current.metrics.items()},
+        )
+
+    def display_name(self) -> str:
+        return "Recall metric"
+
+
+class RocAucMetric(Metric[ByLabelValue]):
+    def __init__(self, probas_threshold: Optional[float] = None, k: Optional[int] = None):
+        super().__init__("roc_auc")
+        self.probas_threshold = probas_threshold
+        self.k = k
+
+    def calculate(self, current_data: Dataset, reference_data: Optional[Dataset]) -> TResult:
+        raise ValueError()
+
+    def _call(self, context: Context) -> ByLabelValue:
+        result = context.get_legacy_metric(ClassificationQualityByClass(self.probas_threshold, self.k))
+
+        return ByLabelValue(
+            {k: v.roc_auc for k, v in result.current.metrics.items()},
+        )
+
+    def display_name(self) -> str:
+        return "ROC AUC metric"