diff --git a/sklearn/decomposition/_online_lda_fast.pyx b/sklearn/decomposition/_online_lda_fast.pyx
index 446232a57f084..f9b00346b8b88 100644
--- a/sklearn/decomposition/_online_lda_fast.pyx
+++ b/sklearn/decomposition/_online_lda_fast.pyx
@@ -1,22 +1,22 @@
 cimport cython
-cimport numpy as np
+cimport numpy as cnp
 import numpy as np
 
-np.import_array()
+cnp.import_array()
 
 from libc.math cimport exp, fabs, log
 from numpy.math cimport EULER
 
 
-def mean_change(np.ndarray[ndim=1, dtype=np.float64_t] arr_1,
-                np.ndarray[ndim=1, dtype=np.float64_t] arr_2):
+def mean_change(cnp.ndarray[ndim=1, dtype=cnp.float64_t] arr_1,
+                cnp.ndarray[ndim=1, dtype=cnp.float64_t] arr_2):
     """Calculate the mean difference between two arrays.
 
     Equivalent to np.abs(arr_1 - arr2).mean().
     """
 
-    cdef np.float64_t total, diff
-    cdef np.npy_intp i, size
+    cdef cnp.float64_t total, diff
+    cdef cnp.npy_intp i, size
 
     size = arr_1.shape[0]
     total = 0.0
@@ -27,9 +27,9 @@ def mean_change(np.ndarray[ndim=1, dtype=np.float64_t] arr_1,
     return total / size
 
 
-def _dirichlet_expectation_1d(np.ndarray[ndim=1, dtype=np.float64_t] doc_topic,
+def _dirichlet_expectation_1d(cnp.ndarray[ndim=1, dtype=cnp.float64_t] doc_topic,
                               double doc_topic_prior,
-                              np.ndarray[ndim=1, dtype=np.float64_t] out):
+                              cnp.ndarray[ndim=1, dtype=cnp.float64_t] out):
     """Dirichlet expectation for a single sample:
         exp(E[log(theta)]) for theta ~ Dir(doc_topic)
     after adding doc_topic_prior to doc_topic, in-place.
@@ -39,8 +39,8 @@ def _dirichlet_expectation_1d(np.ndarray[ndim=1, dtype=np.float64_t] doc_topic,
         out[:] = np.exp(psi(doc_topic) - psi(np.sum(doc_topic)))
     """
 
-    cdef np.float64_t dt, psi_total, total
-    cdef np.npy_intp i, size
+    cdef cnp.float64_t dt, psi_total, total
+    cdef cnp.npy_intp i, size
 
     size = doc_topic.shape[0]
 
@@ -55,7 +55,7 @@ def _dirichlet_expectation_1d(np.ndarray[ndim=1, dtype=np.float64_t] doc_topic,
         out[i] = exp(psi(doc_topic[i]) - psi_total)
 
 
-def _dirichlet_expectation_2d(np.ndarray[ndim=2, dtype=np.float64_t] arr):
+def _dirichlet_expectation_2d(cnp.ndarray[ndim=2, dtype=cnp.float64_t] arr):
     """Dirichlet expectation for multiple samples:
     E[log(theta)] for theta ~ Dir(arr).
 
@@ -64,9 +64,9 @@ def _dirichlet_expectation_2d(np.ndarray[ndim=2, dtype=np.float64_t] arr):
     Note that unlike _dirichlet_expectation_1d, this function doesn't compute
     the exp and doesn't add in the prior.
     """
-    cdef np.float64_t row_total, psi_row_total
-    cdef np.ndarray[ndim=2, dtype=np.float64_t] d_exp
-    cdef np.npy_intp i, j, n_rows, n_cols
+    cdef cnp.float64_t row_total, psi_row_total
+    cdef cnp.ndarray[ndim=2, dtype=cnp.float64_t] d_exp
+    cdef cnp.npy_intp i, j, n_rows, n_cols
 
     n_rows = arr.shape[0]
     n_cols = arr.shape[1]