Update app.py

app.py

@@ -24,15 +24,15 @@ def compare_lasso_dense():
   # print(f"Sparse Lasso done in {(time() - t0):.3f}s")
   elapse1 = time() - t0
 
-  t0 = time()
+  t1 = time()
   dense_lasso.fit(X, y)
   # print(f"Dense Lasso done in {(time() - t0):.3f}s")
-  elapse2 = time() - t0
+  elapse2 = time() - t1
 
   # compare the regression coefficients
   coeff_diff = linalg.norm(sparse_lasso.coef_ - dense_lasso.coef_)
   # print(f"Distance between coefficients : {coeff_diff:.2e}")
-  return f"Sparse Lasso done in {(time() - t0):.3f}s\t\n" + f"Dense Lasso done in {(time() - t0):.3f}s\t\n" + f"Distance between coefficients : {coeff_diff:.2e}\t\n"
+  return f"Sparse Lasso done in {(elapse1):.3f}s\t\n" + f"Dense Lasso done in {(elapse2):.3f}s\t\n" + f"Distance between coefficients : {coeff_diff:.2e}\t\n"
 
 def compare_lasso_sparse():
   # make a copy of the previous data
@@ -57,15 +57,15 @@ def compare_lasso_sparse():
   print(f"Sparse Lasso done in {(time() - t0):.3f}s")
   elapses1 = time() - t0
 
-  t0 = time()
+  t1 = time()
   dense_lasso.fit(Xs, y)
-  print(f"Dense Lasso done in  {(time() - t0):.3f}s")
-  elapses2 = time() - t0
+  print(f"Dense Lasso done in  {(time() - t1):.3f}s")
+  elapses2 = time() - t1
 
   # compare the regression coefficients
   coeff_diff = linalg.norm(sparse_lasso.coef_ - dense_lasso.coef_)
   print(f"Distance between coefficients : {coeff_diff:.2e}")
-  return f"Matrix density : {(Xs_sp.nnz / float(X.size) * 100):.3f}%\t\n"+ f"Sparse Lasso done in {(time() - t0):.3f}s\t\n" + f"Dense Lasso done in  {(time() - t0):.3f}s\t\n" + f"Distance between coefficients : {coeff_diff:.2e}\t\n" 
+  return f"Matrix density : {(Xs_sp.nnz / float(X.size) * 100):.3f}%\t\n"+ f"Sparse Lasso done in {(elapse1):.3f}s\t\n" + f"Dense Lasso done in  {(elapse2):.3f}s\t\n" + f"Distance between coefficients : {coeff_diff:.2e}\t\n" 
 
 
 X,X_sp,y = load_dataset()