Added n_features_in_ to three of the embedded class estimators (continued from #434)

graspy/embed/ase.py

@@ -78,6 +78,8 @@ class AdjacencySpectralEmbed(BaseEmbed):
 
     Attributes
     ----------
+    n_features_in_: int
+        number of features passed to the fit method.
     latent_left_ : array, shape (n_samples, n_components)
         Estimated left latent positions of the graph.
     latent_right_ : array, shape (n_samples, n_components), or None
@@ -159,5 +161,6 @@ def fit(self, graph, y=None):
         if self.diag_aug:
             A = augment_diagonal(A)
 
+        self.n_features_in_ = len(A)
         self._reduce_dim(A)
         return self

graspy/embed/base.py

@@ -58,6 +58,8 @@ class BaseEmbed(BaseEstimator):
     ----------
     n_components_ : int
         Dimensionality of the embedded space.
+    n_features_in_: int
+        number of features passed to the fit method.
 
     See Also
     --------

graspy/embed/lse.py

@@ -73,6 +73,9 @@ class LaplacianSpectralEmbed(BaseEmbed):
 
     Attributes
     ----------
+    n_features_in_: int
+        number of features passed to the fit method.
+
     latent_left_ : array, shape (n_samples, n_components)
         Estimated left latent positions of the graph.
 
@@ -162,6 +165,7 @@ def fit(self, graph, y=None):
                 )
                 warnings.warn(msg, UserWarning)
 
+        self.n_features_in_ = len(A)
         L_norm = to_laplace(A, form=self.form, regularizer=self.regularizer)
         self._reduce_dim(L_norm)
         return self

graspy/embed/mds.py

@@ -71,6 +71,9 @@ class ClassicalMDS(BaseEstimator):
         Equals the parameter n_components. If input n_components was None,
         then equals the optimal embedding dimension.
 
+    n_features_in_: int
+        number of features passed to the fit method.
+
     components_ : array, shape (n_components, n_features)
         Principal axes in feature space.
 
@@ -203,6 +206,7 @@ def fit(self, X, y=None):
         self.components_ = U
         self.singular_values_ = D ** 0.5
         self.dissimilarity_matrix_ = dissimilarity_matrix
+        self.n_features_in_ = X.shape[1]
 
         return self