Revert "Makes data validator API consistent with other type-checking"

This reverts commit d903745.

hamilton/data_quality/base.py

@@ -35,27 +35,13 @@ def __init__(self, importance: str):
     def importance(self) -> DataValidationLevel:
         return self._importance
 
-    @classmethod
-    def applies_to(cls, datatype: Type[Type]) -> bool:
-        """Whether or not this data validator can apply to the specified dataset.
-        Note that overriding this is not the intended API (it was the old one),
-        but this will be a stable part of the API moving forward, at least until
-        Hamilton 2.0.
+    @abc.abstractmethod
+    def applies_to(self, datatype: Type[Type]) -> bool:
+        """Whether or not this data validator can apply to the specified dataset
 
-        :param datatype: Datatype to validate.
+        :param datatype:
         :return: True if it can be run on the specified type, false otherwise
         """
-        for type_ in cls.applicable_types():
-            if type_ == Any or issubclass(type_, datatype):
-                return True
-        return False
-
-    @classmethod
-    def applicable_types(cls) -> List[type]:
-        """Returns the list of classes for which this is valid.
-
-        :return: List of classes
-        """
         pass
 
     @abc.abstractmethod
@@ -132,7 +118,7 @@ def __init__(self, importance: str):
 
     @classmethod
     @abc.abstractmethod
-    def applicable_types(cls) -> List[type]:
+    def applies_to(cls, datatype: Type[Type]) -> bool:
         pass
 
     @abc.abstractmethod

hamilton/data_quality/default_validators.py

@@ -24,8 +24,8 @@ def arg(cls) -> str:
         return "range"
 
     @classmethod
-    def applicable_types(cls) -> List[type]:
-        return [pd.Series]
+    def applies_to(cls, datatype: Type[Type]) -> bool:
+        return issubclass(datatype, pd.Series)  # TODO -- handle dataframes?
 
     def description(self) -> str:
         return f"Validates that the datapoint falls within the range ({self.range[0]}, {self.range[1]})"
@@ -69,8 +69,8 @@ def arg(cls) -> str:
         return "values_in"
 
     @classmethod
-    def applicable_types(cls) -> List[type]:
-        return [pd.Series]
+    def applies_to(cls, datatype: Type[Type]) -> bool:
+        return issubclass(datatype, pd.Series)  # TODO -- handle dataframes?
 
     def description(self) -> str:
         return f"Validates that all data points are from a fixed set of values: ({self.values}), ignoring NA values."
@@ -113,8 +113,8 @@ def __init__(self, range: Tuple[numbers.Real, numbers.Real], importance: str):
         self.range = range
 
     @classmethod
-    def applicable_types(cls) -> List[type]:
-        return [numbers.Real]
+    def applies_to(cls, datatype: Type[Type]) -> bool:
+        return issubclass(datatype, numbers.Real)
 
     def description(self) -> str:
         return f"Validates that the datapoint falls within the range ({self.range[0]}, {self.range[1]})"
@@ -151,8 +151,10 @@ def arg(cls) -> str:
         return "values_in"
 
     @classmethod
-    def applicable_types(cls) -> List[type]:
-        return [numbers.Real, str]
+    def applies_to(cls, datatype: Type[Type]) -> bool:
+        return issubclass(datatype, numbers.Real) or issubclass(
+            datatype, str
+        )  # TODO support list, dict and typing.* variants
 
     def description(self) -> str:
         return f"Validates that python values are from a fixed set of values: ({self.values})."
@@ -187,8 +189,8 @@ def _to_percent(fraction: float):
         return "{0:.2%}".format(fraction)
 
     @classmethod
-    def applicable_types(cls) -> List[type]:
-        return [pd.Series]
+    def applies_to(cls, datatype: Type[Type]) -> bool:
+        return issubclass(datatype, pd.Series)
 
     def description(self) -> str:
         return f"Validates that no more than {MaxFractionNansValidatorPandasSeries._to_percent(self.max_fraction_nans)} of the data is Nan."
@@ -249,8 +251,8 @@ def __init__(self, data_type: Type[Type], importance: str):
         self.datatype = data_type
 
     @classmethod
-    def applicable_types(cls) -> List[type]:
-        return [pd.Series]
+    def applies_to(cls, datatype: Type[Type]) -> bool:
+        return issubclass(datatype, pd.Series)
 
     def description(self) -> str:
         return f"Validates that the datatype of the pandas series is a subclass of: {self.datatype}"
@@ -280,8 +282,8 @@ def __init__(self, data_type: Type[Type], importance: str):
         self.datatype = data_type
 
     @classmethod
-    def applicable_types(cls) -> List[type]:
-        return [numbers.Real, str, bool, int, float, list, dict]
+    def applies_to(cls, datatype: Type[Type]) -> bool:
+        return issubclass(datatype, numbers.Real) or datatype in (str, bool)
 
     def description(self) -> str:
         return f"Validates that the datatype of the pandas series is a subclass of: {self.datatype}"
@@ -310,8 +312,8 @@ def __init__(self, max_standard_dev: float, importance: str):
         self.max_standard_dev = max_standard_dev
 
     @classmethod
-    def applicable_types(cls) -> List[type]:
-        return [pd.Series]
+    def applies_to(cls, datatype: Type[Type]) -> bool:
+        return issubclass(datatype, pd.Series)
 
     def description(self) -> str:
         return f"Validates that the standard deviation of a pandas series is no greater than : {self.max_standard_dev}"
@@ -338,8 +340,8 @@ def __init__(self, mean_in_range: Tuple[float, float], importance: str):
         self.mean_in_range = mean_in_range
 
     @classmethod
-    def applicable_types(cls) -> List[type]:
-        return [pd.Series]
+    def applies_to(cls, datatype: Type[Type]) -> bool:
+        return issubclass(datatype, pd.Series)
 
     def description(self) -> str:
         return f"Validates that a pandas series has mean in range [{self.mean_in_range[0]}, {self.mean_in_range[1]}]"
@@ -366,8 +368,8 @@ def __init__(self, allow_none: bool, importance: str):
         self.allow_none = allow_none
 
     @classmethod
-    def applicable_types(cls) -> List[type]:
-        return [Any]
+    def applies_to(cls, datatype: Type[Type]) -> bool:
+        return True
 
     def description(self) -> str:
         if self.allow_none:

hamilton/data_quality/pandera_validators.py

@@ -1,4 +1,4 @@
-from typing import List
+from typing import Type
 
 import pandas as pd
 import pandera as pa
@@ -14,8 +14,10 @@ def __init__(self, schema: pa.DataFrameSchema, importance: str):
         self.schema = schema
 
     @classmethod
-    def applicable_types(cls) -> List[type]:
-        return [pd.DataFrame]
+    def applies_to(cls, datatype: Type[Type]) -> bool:
+        return issubclass(
+            datatype, pd.DataFrame
+        )  # TODO -- allow for modin, etc. as they come for free with pandera
 
     def description(self) -> str:
         return "Validates that the returned dataframe matches the pander"
@@ -52,8 +54,10 @@ def __init__(self, schema: pa.SeriesSchema, importance: str):
         self.schema = schema
 
     @classmethod
-    def applicable_types(cls) -> List[type]:
-        return [pd.Series]
+    def applies_to(cls, datatype: Type[Type]) -> bool:
+        return issubclass(
+            datatype, pd.Series
+        )  # TODO -- allow for modin, etc. as they come for free with pandera
 
     def description(self) -> str:
         pass

tests/resources/dq_dummy_examples.py

@@ -1,4 +1,4 @@
-from typing import List
+from typing import Type
 
 import pandas as pd
 
@@ -11,8 +11,8 @@ def __init__(self, equal_to: int, importance: str):
         self.equal_to = equal_to
 
     @classmethod
-    def applicable_types(cls) -> List[type]:
-        return [int]
+    def applies_to(cls, datatype: Type[Type]) -> bool:
+        return datatype == int
 
     def description(self) -> str:
         return "Data must be equal to 10 to be valid"
@@ -60,8 +60,8 @@ def validate(self, dataset: pd.Series) -> ValidationResult:
         )
 
     @classmethod
-    def applicable_types(cls) -> List[type]:
-        return [pd.Series]
+    def applies_to(cls, datatype: Type[Type]) -> bool:
+        return datatype == pd.Series
 
     @classmethod
     def arg(cls) -> str:
@@ -92,8 +92,8 @@ def validate(self, dataset: pd.Series) -> ValidationResult:
         )
 
     @classmethod
-    def applicable_types(cls) -> List[type]:
-        return [pd.Series]
+    def applies_to(cls, datatype: Type[Type]) -> bool:
+        return datatype == pd.Series
 
     @classmethod
     def arg(cls) -> str: