Fix use of deprecated validate_data

src/sklearn_ann/cluster/rnn_dbscan.py

@@ -5,6 +5,7 @@
 from sklearn.base import BaseEstimator, ClusterMixin
 from sklearn.neighbors import KNeighborsTransformer
 from sklearn.utils import Tags
+from sklearn.utils.validation import validate_data
 
 from ..utils import get_sparse_row
 
@@ -145,7 +146,7 @@ def __init__(
         self.keep_knns = keep_knns
 
     def fit(self, X, y=None):
-        X = self._validate_data(X, accept_sparse="csr")
+        X = validate_data(self, X, accept_sparse="csr")
         if self.input_guarantee == "none":
             algorithm = KNeighborsTransformer(n_neighbors=self.n_neighbors)
             X = algorithm.fit_transform(X)

src/sklearn_ann/kneighbors/annoy.py

@@ -3,6 +3,7 @@
 from scipy.sparse import csr_matrix
 from sklearn.base import BaseEstimator, TransformerMixin
 from sklearn.utils import Tags, TargetTags, TransformerTags
+from sklearn.utils.validation import validate_data
 
 from ..utils import TransformerChecksMixin
 
@@ -17,7 +18,7 @@ def __init__(self, n_neighbors=5, *, metric="euclidean", n_trees=10, search_k=-1
         self.metric = metric
 
     def fit(self, X, y=None):
-        X = self._validate_data(X)
+        X = validate_data(self, X)
         self.n_samples_fit_ = X.shape[0]
         metric = self.metric if self.metric != "sqeuclidean" else "euclidean"
         self.annoy_ = annoy.AnnoyIndex(X.shape[1], metric=metric)

src/sklearn_ann/kneighbors/faiss.py

@@ -9,6 +9,7 @@
 from scipy.sparse import csr_matrix
 from sklearn.base import BaseEstimator, TransformerMixin
 from sklearn.utils import Tags, TargetTags, TransformerTags
+from sklearn.utils.validation import validate_data
 
 from ..utils import TransformerChecksMixin, postprocess_knn_csr
 
@@ -86,7 +87,7 @@ def _metric_info(self):
 
     def fit(self, X, y=None):
         normalize = self._metric_info.get("normalize", False)
-        X = self._validate_data(X, dtype=np.float32, copy=normalize)
+        X = validate_data(self, X, dtype=np.float32, copy=normalize)
         self.n_samples_fit_ = X.shape[0]
         if self.n_jobs == -1:
             n_jobs = cpu_count()

src/sklearn_ann/kneighbors/nmslib.py

@@ -3,6 +3,7 @@
 from scipy.sparse import csr_matrix
 from sklearn.base import BaseEstimator, TransformerMixin
 from sklearn.utils import Tags, TransformerTags
+from sklearn.utils.validation import validate_data
 
 from ..utils import TransformerChecksMixin, check_metric
 
@@ -29,7 +30,7 @@ def __init__(
         self.n_jobs = n_jobs
 
     def fit(self, X, y=None):
-        X = self._validate_data(X)
+        X = validate_data(self, X)
         self.n_samples_fit_ = X.shape[0]
 
         check_metric(self.metric, METRIC_MAP)

src/sklearn_ann/utils.py

@@ -1,5 +1,6 @@
 import numpy as np
 from scipy.sparse import csr_matrix
+from sklearn.utils.validation import validate_data
 
 
 def check_metric(metric, metrics):
@@ -90,6 +91,6 @@ class TransformerChecksMixin:
     def _transform_checks(self, X, *fitted_props, **check_params):
         from sklearn.utils.validation import check_is_fitted
 
-        X = self._validate_data(X, reset=False, **check_params)
+        X = validate_data(self, X, reset=False, **check_params)
         check_is_fitted(self, *fitted_props)
         return X