Merge pull request #8 from kolmogorov-lab/hotfix

Hotfix

.gitignore

@@ -113,6 +113,7 @@ venv/
 ENV/
 env.bak/
 venv.bak/
+abacus_env/
 
 # Spyder project settings
 .spyderproject

abacus/auto_ab/params.py

@@ -1,10 +1,13 @@
 from __future__ import annotations
-from typing import Dict, Any, Optional
+from typing import Dict, Any, Optional, List, Tuple
 
 from pydantic.dataclasses import dataclass
 from pydantic import validator, Field
 import numpy as np
 
+
+
+
 from abacus.types import (
     ColumnNameType,
     ColumnNamesType,
@@ -14,6 +17,24 @@
     MetricTransformType,
 )
 
+class MismatchMetricError(Exception):
+    """
+    Checks if user assigns custom metric functin along with custom metric name
+    Args:
+    metric_name:str
+    Metric name assigned by user via metric_name attr
+    msg: str
+    Error message template (default: 'You use default metric function with custom metric name "{}"! Reassign it with "metric" parameter.')
+    """
+    def __init__(
+        self,
+        metric_name:str,
+        msg:str='You use default metric function with custom metric name "{}"! Reassign it with "metric" parameter.'
+        ):
+        self.metric_name = metric_name
+        self.msg = msg.format(self.metric_name)
+        super().__init__(self.msg)
+
 
 class ValidationConfig:
     validate_assignment = True
@@ -79,8 +100,9 @@ class HypothesisParams:
         n_buckets (int, Optional): number of buckets.
         strata (str, Optional): stratification column.
         strata_weights (Dict[str, float], Optional): historical strata weights.
+        default_metrics: Optional[Tuple[str]] : default metric with autoselected metric functions
     """
-
+    
     alpha: Optional[float] = 0.05
     beta: Optional[float] = 0.2
     alternative: Optional[str] = "two-sided"  # less, greater, two-sided
@@ -96,12 +118,17 @@ class HypothesisParams:
     n_buckets: Optional[int] = 100
     strata: Optional[str] = ""
     strata_weights: Optional[Dict[str, float]] = Field(default_factory=dict)
+    default_metrics: Optional[Tuple[str, ...]] = ('mean', 'median')
 
     def __post_init__(self):
+        if self.metric_name not in self.default_metrics \
+            and self.metric is np.mean:
+                raise MismatchMetricError(self.metric_name)
         if self.metric_name == "mean":
             self.metric = np.mean
         if self.metric_name == "median":
             self.metric = np.median
+
 
     @validator("alpha", always=True, allow_reuse=True)
     @classmethod