[python] improving the syntax of the fstring in the file :...

[python] improving the syntax of the fstring in the file : tests/python_package_test/test_dask.py (#4358)

* updated the old syntax with fstrings

* Updated the strings with + catenation to fstrings

* Updated the strings with + catenation to fstrings

* Update tests/python_package_test/test_dask.py
Co-authored-by: James Lamb <jaylamb20@gmail.com>

tests/python_package_test/test_dask.py

@@ -95,7 +95,7 @@ def _create_ranking_data(n_samples=100, output='array', chunk_size=50, **kwargs)
         X_df = pd.DataFrame(X, columns=[f'feature_{i}' for i in range(X.shape[1])])
         if output == 'dataframe-with-categorical':
             for i in range(5):
-                col_name = "cat_col" + str(i)
+                col_name = f"cat_col{i}"
                 cat_values = rnd.choice(['a', 'b'], X.shape[0])
                 cat_series = pd.Series(
                     cat_values,
@@ -159,7 +159,7 @@ def _create_data(objective, n_samples=1_000, output='array', chunk_size=500, **k
             **kwargs
         )
     else:
-        raise ValueError("Unknown objective '%s'" % objective)
+        raise ValueError(f"Unknown objective '{objective}'")
     rnd = np.random.RandomState(42)
     weights = rnd.random(X.shape[0]) * 0.01
 
@@ -168,11 +168,11 @@ def _create_data(objective, n_samples=1_000, output='array', chunk_size=500, **k
         dy = da.from_array(y, chunk_size)
         dw = da.from_array(weights, chunk_size)
     elif output.startswith('dataframe'):
-        X_df = pd.DataFrame(X, columns=['feature_%d' % i for i in range(X.shape[1])])
+        X_df = pd.DataFrame(X, columns=[f'feature_{i}' for i in range(X.shape[1])])
         if output == 'dataframe-with-categorical':
             num_cat_cols = 2
             for i in range(num_cat_cols):
-                col_name = "cat_col" + str(i)
+                col_name = f"cat_col{i}"
                 cat_values = rnd.choice(['a', 'b'], X.shape[0])
                 cat_series = pd.Series(
                     cat_values,
@@ -199,7 +199,7 @@ def _create_data(objective, n_samples=1_000, output='array', chunk_size=500, **k
         dy = da.from_array(y, chunks=chunk_size)
         dw = da.from_array(weights, chunk_size)
     else:
-        raise ValueError("Unknown output type '%s'" % output)
+        raise ValueError("Unknown output type '{output}'")
 
     return X, y, weights, None, dX, dy, dw, None
 
@@ -1105,7 +1105,7 @@ def test_network_params_not_required_but_respected_if_given(task, listen_port, c
             n_estimators=5,
             num_leaves=5,
             machines=",".join([
-                "127.0.0.1:" + str(port)
+                f"127.0.0.1:{port}"
                 for port in open_ports
             ]),
         )
@@ -1151,14 +1151,14 @@ def test_machines_should_be_used_if_provided(task, cluster):
             n_estimators=5,
             num_leaves=5,
             machines=",".join([
-                "127.0.0.1:" + str(port)
+                f"127.0.0.1:{port}"
                 for port in open_ports
             ]),
         )
 
         # test that "machines" is actually respected by creating a socket that uses
         # one of the ports mentioned in "machines"
-        error_msg = "Binding port %s failed" % open_ports[0]
+        error_msg = f"Binding port {open_ports[0]} failed"
         with pytest.raises(lgb.basic.LightGBMError, match=error_msg):
             with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
                 s.bind(('127.0.0.1', open_ports[0]))
@@ -1171,7 +1171,7 @@ def test_machines_should_be_used_if_provided(task, cluster):
         one_open_port = lgb.dask._find_random_open_port()
         dask_model.set_params(
             machines=",".join([
-                "127.0.0.1:" + str(one_open_port)
+                f"127.0.0.1:{one_open_port}"
                 for _ in range(n_workers)
             ])
         )