Merge pull request #241 from IntelPython/fix/flake8_exclusions

Fix complexity violations highlighted by flake8

Author: adarshyoga

.flake8

@@ -8,8 +8,4 @@ exclude =
     __pycache__
     ./dist
     ./setup.py
-    ./dpbench/benchmarks/l2_norm/l2_norm_numba_dpex_k.py
-    ./dpbench/benchmarks/pairwise_distance/pairwise_distance_numba_dpex_k.py
-    ./dpbench/benchmarks/pairwise_distance/pairwise_distance_numba_dpex_p.py
-    ./dpbench/benchmarks/pairwise_distance/pairwise_distance_numba_npr.py
 max-complexity = 10

dpbench/benchmarks/l2_norm/l2_norm_numba_dpex_k.py

@@ -9,9 +9,9 @@
 @dpex.kernel
 def l2_norm_kernel(a, d):
     i = dpex.get_global_id(0)
-    O = a.shape[1]
+    a_rows = a.shape[1]
     d[i] = 0.0
-    for k in range(O):
+    for k in range(a_rows):
         d[i] += a[i, k] * a[i, k]
     d[i] = np.sqrt(d[i])
 

dpbench/benchmarks/pairwise_distance/pairwise_distance_numba_dpex_k.py

@@ -10,11 +10,11 @@
 def _pairwise_distance_kernel(X1, X2, D):
     i = dpex.get_global_id(0)
 
-    N = X2.shape[0]
-    O = X1.shape[1]
-    for j in range(N):
+    X2_rows = X2.shape[0]
+    X1_cols = X1.shape[1]
+    for j in range(X2_rows):
         d = 0.0
-        for k in range(O):
+        for k in range(X1_cols):
             tmp = X1[i, k] - X2[j, k]
             d += tmp * tmp
         D[i, j] = np.sqrt(d)

dpbench/benchmarks/pairwise_distance/pairwise_distance_numba_dpex_p.py

@@ -18,17 +18,17 @@ def pairwise_distance(X1, X2, D):
         D  : Outputted distance matrix
     """
     # Size of inputs
-    M = X1.shape[0]
-    N = X2.shape[0]
-    O = X1.shape[1]
+    X1_rows = X1.shape[0]
+    X2_rows = X2.shape[0]
+    X1_cols = X1.shape[1]
 
     # Outermost parallel loop over the matrix X1
-    for i in nb.prange(M):
+    for i in nb.prange(X1_rows):
         # Loop over the matrix X2
-        for j in range(N):
+        for j in range(X2_rows):
             d = 0.0
             # Compute exclidean distance
-            for k in range(O):
+            for k in range(X1_cols):
                 tmp = X1[i, k] - X2[j, k]
                 d += tmp * tmp
             # Write computed distance to distance matrix

dpbench/benchmarks/pairwise_distance/pairwise_distance_numba_npr.py

@@ -8,13 +8,13 @@
 
 @nb.njit(parallel=True, fastmath=True)
 def pairwise_distance(X1, X2, D):
-    M = X1.shape[0]
-    N = X2.shape[0]
-    O = X1.shape[1]
-    for i in nb.prange(M):
-        for j in range(N):
+    X1_rows = X1.shape[0]
+    X2_rows = X2.shape[0]
+    X1_cols = X1.shape[1]
+    for i in nb.prange(X1_rows):
+        for j in range(X2_rows):
             d = 0.0
-            for k in range(O):
+            for k in range(X1_cols):
                 tmp = X1[i, k] - X2[j, k]
                 d += tmp * tmp
             D[i, j] = np.sqrt(d)