Add estimator parameters filter

Author: avolkov-intel

sklbench/benchmarks/sklearn_estimator.py

@@ -334,6 +334,48 @@ def verify_patching(stream: io.StringIO, function_name) -> bool:
     return acceleration_lines > 0 and fallback_lines == 0
 
 
+def validate_estimator_params(estimator_class, estimator_params: Dict) -> Dict:
+    """Validates parameters and returns only those supported by the estimator.
+
+    Args:
+        estimator_class: The estimator class to validate against
+        estimator_params: Dictionary of parameters to validate
+
+    Returns:
+        Dictionary with only valid parameters
+    """
+    try:
+        init_signature = inspect.signature(estimator_class.__init__)
+        valid_params = set(init_signature.parameters.keys()) - {"self"}
+
+        # Check if estimator accepts **kwargs
+        has_var_keyword = any(
+            param.kind == inspect.Parameter.VAR_KEYWORD
+            for param in init_signature.parameters.values()
+        )
+
+        # If accepts **kwargs, return all params
+        if has_var_keyword:
+            return estimator_params
+
+        # Filter out invalid params and warn
+        filtered_params = {}
+        for param_name, param_value in estimator_params.items():
+            if param_name in valid_params:
+                filtered_params[param_name] = param_value
+            else:
+                logger.warning(
+                    f"Parameter '{param_name}' is not supported by "
+                    f"{estimator_class.__name__} and will be ignored"
+                )
+
+        return filtered_params
+
+    except Exception as e:
+        logger.debug(f"Could not validate parameters for {estimator_class.__name__}: {e}")
+        return estimator_params
+
+
 def create_online_function(method_instance, data_args, batch_size):
     n_batches = data_args[0].shape[0] // batch_size
 
@@ -491,6 +533,9 @@ def main(bench_case: BenchCase, filters: List[BenchCase]):
         bench_case, "algorithm:estimator_params", dict()
     )
 
+    # validate and filter estimator parameters
+    estimator_params = validate_estimator_params(estimator_class, estimator_params)
+
     # get estimator methods for measurement
     estimator_methods = get_estimator_methods(bench_case)
 

sklbench/datasets/common.py

@@ -168,7 +168,7 @@ def preprocess_x(
     x: Array,
     replace_nan="auto",
     category_encoding="ordinal",
-    normalize=None, # None, "standard", "minmax"
+    normalize=None,
     force_for_sparse=True,
     **kwargs,
 ) -> Array:
@@ -223,13 +223,10 @@ def preprocess_x(
     # Normalization
     if normalize:
         if normalize == "standard":
-            #x = (x - x.mean()) / x.std()
             scaler = StandardScaler(with_mean=True, with_std=True)
         elif normalize == "mean":
-            #x = x - x.mean()
             scaler = StandardScaler(with_mean=True, with_std=False)
         elif normalize == "minmax":
-            #x = (x - x.min()) / (x.max() - x.min())
             scaler = MinMaxScaler(feature_range=(0, 1))
         else:
             logger.warning(f'Unknown "{normalize}" normalization type.')