refreactored regression metrics to new dir

fuse/eval/examples/examples_stats.py

@@ -17,7 +17,7 @@
 
 """
 
-from fuse.eval.metrics.stat.metrics_stat_common import MetricPearsonCorrelation
+from fuse.eval.metrics.regression.metrics import MetricPearsonCorrelation
 import numpy as np
 import pandas as pd
 from collections import OrderedDict

fuse/eval/metrics/regression/metrics.py

@@ -0,0 +1,131 @@
+from typing import List, Optional, Union
+from fuse.eval.metrics.libs.stat import Stat
+from fuse.eval.metrics.metrics_common import MetricDefault
+import numpy as np
+from sklearn.metrics import mean_absolute_error, mean_squared_error
+
+
+class MetricPearsonCorrelation(MetricDefault):
+    def __init__(
+        self, pred: str, target: str, mask: Optional[str] = None, **kwargs: dict
+    ) -> None:
+        super().__init__(
+            pred=pred,
+            target=target,
+            mask=mask,
+            metric_func=Stat.pearson_correlation,
+            **kwargs,
+        )
+
+
+class MetricSpearmanCorrelation(MetricDefault):
+    def __init__(
+        self, pred: str, target: str, mask: Optional[str] = None, **kwargs: dict
+    ) -> None:
+        super().__init__(
+            pred=pred,
+            target=target,
+            mask=mask,
+            metric_func=Stat.spearman_correlation,
+            **kwargs,
+        )
+
+
+class MetricMAE(MetricDefault):
+    def __init__(
+        self,
+        pred: str,
+        target: str,
+        **kwargs: dict,
+    ) -> None:
+        """
+        See MetricDefault for the missing params
+        :param pred: scalar predictions
+        :param target: ground truth scalar labels
+        :param threshold: threshold to apply to both pred and target
+        :param balanced: optionally to use balanced accuracy (from sklearn) instead of regular accuracy.
+        """
+        super().__init__(
+            pred=pred,
+            target=target,
+            metric_func=self.mae,
+            **kwargs,
+        )
+
+    def mae(
+        self,
+        pred: Union[List, np.ndarray],
+        target: Union[List, np.ndarray],
+        **kwargs: dict,
+    ) -> float:
+        return mean_absolute_error(y_true=target, y_pred=pred)
+
+
+class MetricMSE(MetricDefault):
+    def __init__(
+        self,
+        pred: str,
+        target: str,
+        **kwargs: dict,
+    ) -> None:
+        """
+        Our implementation of standard MSE, current version of scikit dones't support it as a metric.
+        See MetricDefault for the missing params
+        :param pred: scalar predictions
+        :param target: ground truth scalar labels
+        :param threshold: threshold to apply to both pred and target
+        :param balanced: optionally to use balanced accuracy (from sklearn) instead of regular accuracy.
+        """
+        super().__init__(
+            pred=pred,
+            target=target,
+            metric_func=self.mse,
+            **kwargs,
+        )
+
+    def mse(
+        self,
+        pred: Union[List, np.ndarray],
+        target: Union[List, np.ndarray],
+        **kwargs: dict,
+    ) -> float:
+        return mean_squared_error(y_true=target, y_pred=pred)
+
+
+class MetricRMSE(MetricDefault):
+    def __init__(
+        self,
+        pred: str,
+        target: str,
+        **kwargs: dict,
+    ) -> None:
+        """
+        See MetricDefault for the missing params
+        :param pred: scalar predictions
+        :param target: ground truth scalar labels
+        :param threshold: threshold to apply to both pred and target
+        :param balanced: optionally to use balanced accuracy (from sklearn) instead of regular accuracy.
+        """
+        super().__init__(
+            pred=pred,
+            target=target,
+            metric_func=self.mse,
+            **kwargs,
+        )
+
+    def mse(
+        self,
+        pred: Union[List, np.ndarray],
+        target: Union[List, np.ndarray],
+        **kwargs: dict,
+    ) -> float:
+
+        pred = np.array(pred).flatten()
+        target = np.array(target).flatten()
+
+        assert len(pred) == len(
+            target
+        ), f"Expected pred and target to have the dimensions but found: {len(pred)} elements in pred and {len(target)} in target"
+
+        squared_diff = (pred - target) ** 2
+        return squared_diff.mean()

fuse/eval/metrics/stat/metrics_stat_common.py

@@ -1,10 +1,6 @@
-from typing import Any, Dict, Hashable, Optional, Sequence, Union, List
+from typing import Any, Dict, Hashable, Optional, Sequence
 from collections import Counter
-from fuse.eval.metrics.metrics_common import MetricDefault, MetricWithCollectorBase
-from fuse.eval.metrics.libs.stat import Stat
-
-import numpy as np  # is this import an issue here? if so can move to other dir
-from sklearn.metrics import mean_absolute_error, mean_squared_error
+from fuse.eval.metrics.metrics_common import MetricWithCollectorBase
 
 
 class MetricUniqueValues(MetricWithCollectorBase):
@@ -23,129 +19,3 @@ def eval(
         counter = Counter(values)
 
         return list(counter.items())
-
-
-class MetricPearsonCorrelation(MetricDefault):
-    def __init__(
-        self, pred: str, target: str, mask: Optional[str] = None, **kwargs: dict
-    ) -> None:
-        super().__init__(
-            pred=pred,
-            target=target,
-            mask=mask,
-            metric_func=Stat.pearson_correlation,
-            **kwargs,
-        )
-
-
-class MetricSpearmanCorrelation(MetricDefault):
-    def __init__(
-        self, pred: str, target: str, mask: Optional[str] = None, **kwargs: dict
-    ) -> None:
-        super().__init__(
-            pred=pred,
-            target=target,
-            mask=mask,
-            metric_func=Stat.spearman_correlation,
-            **kwargs,
-        )
-
-
-class MetricMAE(MetricDefault):
-    def __init__(
-        self,
-        pred: str,
-        target: str,
-        **kwargs: dict,
-    ) -> None:
-        """
-        See MetricDefault for the missing params
-        :param pred: scalar predictions
-        :param target: ground truth scalar labels
-        :param threshold: threshold to apply to both pred and target
-        :param balanced: optionally to use balanced accuracy (from sklearn) instead of regular accuracy.
-        """
-        super().__init__(
-            pred=pred,
-            target=target,
-            metric_func=self.mae,
-            **kwargs,
-        )
-
-    def mae(
-        self,
-        pred: Union[List, np.ndarray],
-        target: Union[List, np.ndarray],
-        **kwargs: dict,
-    ) -> float:
-        return mean_absolute_error(y_true=target, y_pred=pred)
-
-
-class MetricMSE(MetricDefault):
-    def __init__(
-        self,
-        pred: str,
-        target: str,
-        **kwargs: dict,
-    ) -> None:
-        """
-        Our implementation of standard MSE, current version of scikit dones't support it as a metric.
-        See MetricDefault for the missing params
-        :param pred: scalar predictions
-        :param target: ground truth scalar labels
-        :param threshold: threshold to apply to both pred and target
-        :param balanced: optionally to use balanced accuracy (from sklearn) instead of regular accuracy.
-        """
-        super().__init__(
-            pred=pred,
-            target=target,
-            metric_func=self.mse,
-            **kwargs,
-        )
-
-    def mse(
-        self,
-        pred: Union[List, np.ndarray],
-        target: Union[List, np.ndarray],
-        **kwargs: dict,
-    ) -> float:
-        return mean_squared_error(y_true=target, y_pred=pred)
-
-
-class MetricRMSE(MetricDefault):
-    def __init__(
-        self,
-        pred: str,
-        target: str,
-        **kwargs: dict,
-    ) -> None:
-        """
-        See MetricDefault for the missing params
-        :param pred: scalar predictions
-        :param target: ground truth scalar labels
-        :param threshold: threshold to apply to both pred and target
-        :param balanced: optionally to use balanced accuracy (from sklearn) instead of regular accuracy.
-        """
-        super().__init__(
-            pred=pred,
-            target=target,
-            metric_func=self.mse,
-            **kwargs,
-        )
-
-    def mse(
-        self,
-        pred: Union[List, np.ndarray],
-        target: Union[List, np.ndarray],
-        **kwargs: dict,
-    ) -> float:
-
-        pred = np.array(pred).flatten()
-        target = np.array(target).flatten()
-
-        assert len(pred) == len(
-            target
-        ), f"Expected pred and target to have the dimensions but found: {len(pred)} elements in pred and {len(target)} in target"
-
-        squared_diff = (pred - target) ** 2
-        return squared_diff.mean()