remove old code

onedal/basic_statistics/basic_statistics.py

@@ -28,6 +28,7 @@ class BasicStatistics(BaseEstimator, metaclass=ABCMeta):
     """
     Basic Statistics oneDAL implementation.
     """
+
     @abstractmethod
     def __init__(self, result_options="all", algorithm="by_default"):
         self.options = result_options
@@ -58,7 +59,7 @@ def options(self):
     def options(self, options):
         # options always to be an iterable
         self._options = options.split("|") if isinstance(options, str) else options
-        
+
     def _get_onedal_params(self, is_csr, dtype=np.float32):
         return {
             "fptype": dtype,
@@ -79,13 +80,12 @@ def fit(self, data, sample_weight=None, queue=None):
         module = self._get_backend("basic_statistics")
         params = self._get_onedal_params(is_csr, data_table.dtype)
         result = module.compute(policy, params, data_table, weights_table)
-        
+
         for opt in self.options:
-            value = from_table(getattr(result, opt)).ravel()
+            value = from_table(getattr(result, opt))[:, 0]  # two-dimensional table [n, 1]
             if is_single_dim:
-                setattr(self, getattr(raw_result, opt), value[0])
+                setattr(self, opt, value[0])
             else:
                 setattr(self, opt, value)
 
         return self
-

onedal/basic_statistics/incremental_basic_statistics.py

@@ -16,14 +16,11 @@
 
 import numpy as np
 
-from daal4py.sklearn._utils import get_dtype
-
 from ..datatypes import _convert_to_supported, from_table, to_table
-from ..utils import _check_array
-from .basic_statistics import BaseBasicStatistics
+from .basic_statistics import BasicStatistics
 
 
-class IncrementalBasicStatistics(BaseBasicStatistics):
+class IncrementalBasicStatistics(BasicStatistics):
     """
     Incremental estimator for basic statistics based on oneDAL implementation.
     Allows to compute basic statistics if data are splitted into batches.
@@ -65,16 +62,16 @@ class IncrementalBasicStatistics(BaseBasicStatistics):
             Second order moment of each feature over all samples.
     """
 
-    def __init__(self, result_options="all"):
-        super().__init__(result_options, algorithm="by_default")
+    def __init__(self, result_options="all", algorithm="by_default"):
+        super().__init__(result_options, algorithm)
         self._reset()
 
     def _reset(self):
         self._partial_result = self._get_backend(
             "basic_statistics", None, "partial_compute_result"
         )
 
-    def partial_fit(self, X, weights=None, queue=None):
+    def partial_fit(self, X, sample_weight=None, queue=None):
         """
         Computes partial data for basic statistics
         from data batch X and saves it to `_partial_result`.
@@ -95,33 +92,20 @@ def partial_fit(self, X, weights=None, queue=None):
         """
         self._queue = queue
         policy = self._get_policy(queue, X)
-        X, weights = _convert_to_supported(policy, X, weights)
-
-        X = _check_array(
-            X, dtype=[np.float64, np.float32], ensure_2d=False, force_all_finite=False
-        )
-        if weights is not None:
-            weights = _check_array(
-                weights,
-                dtype=[np.float64, np.float32],
-                ensure_2d=False,
-                force_all_finite=False,
-            )
+        X, sample_weight = to_table(_convert_to_supported(policy, X, sample_weight))
 
         if not hasattr(self, "_onedal_params"):
-            dtype = get_dtype(X)
-            self._onedal_params = self._get_onedal_params(False, dtype=dtype)
+            self._onedal_params = self._get_onedal_params(False, dtype=X.dtype)
 
-        X_table, weights_table = to_table(X, weights)
         self._partial_result = self._get_backend(
             "basic_statistics",
             None,
             "partial_compute",
             policy,
             self._onedal_params,
             self._partial_result,
-            X_table,
-            weights_table,
+            X,
+            sample_weight,
         )
 
     def finalize_fit(self, queue=None):
@@ -153,8 +137,8 @@ def finalize_fit(self, queue=None):
             self._onedal_params,
             self._partial_result,
         )
-        options = self._get_result_options(self.options).split("|")
-        for opt in options:
-            setattr(self, opt, from_table(getattr(result, opt)).ravel())
+
+        for opt in self.options:
+            setattr(self, opt, from_table(getattr(result, opt))[:, 0])
 
         return self