tests: test kwargs for `keras`, `dask`

tests/tests_dask.py

@@ -13,7 +13,8 @@ def test_dask(capsys):
     dask = importorskip('dask')
 
     schedule = [dask.delayed(sleep)(i / 10) for i in range(5)]
-    with ProgressBar():
+    with ProgressBar(desc="computing"):
         dask.compute(schedule)
     _, err = capsys.readouterr()
+    assert "computing: " in err
     assert '5/5' in err

tests/tests_keras.py

@@ -1,14 +1,12 @@
 from __future__ import division
 
-from tqdm import tqdm
+from .tests_tqdm import importorskip, mark
-
-from .tests_tqdm import StringIO, closing, importorskip, mark
 
 pytestmark = mark.slow
 
 
 @mark.filterwarnings("ignore:.*:DeprecationWarning")
-def test_keras():
+def test_keras(capsys):
     """Test tqdm.keras.TqdmCallback"""
     TqdmCallback = importorskip('tqdm.keras').TqdmCallback
     np = importorskip('numpy')
@@ -27,67 +25,58 @@ def test_keras():
     batches = len(x) / batch_size
     epochs = 5
 
-    with closing(StringIO()) as our_file:
+    # just epoch (no batch) progress
+    model.fit(
+        x,
+        x,
+        epochs=epochs,
+        batch_size=batch_size,
+        verbose=False,
+        callbacks=[
+            TqdmCallback(
+                epochs,
+                desc="training",
+                data_size=len(x),
+                batch_size=batch_size,
+                verbose=0,
+            )],
+    )
+    _, res = capsys.readouterr()
+    assert "training: " in res
+    assert "{epochs}/{epochs}".format(epochs=epochs) in res
+    assert "{batches}/{batches}".format(batches=batches) not in res
 
-        class Tqdm(tqdm):
+    # full (epoch and batch) progress
-            """redirected I/O class"""
+    model.fit(
-            def __init__(self, *a, **k):
+        x,
-                k.setdefault("file", our_file)
+        x,
-                super(Tqdm, self).__init__(*a, **k)
+        epochs=epochs,
+        batch_size=batch_size,
+        verbose=False,
+        callbacks=[
+            TqdmCallback(
+                epochs,
+                desc="training",
+                data_size=len(x),
+                batch_size=batch_size,
+                verbose=2,
+            )],
+    )
+    _, res = capsys.readouterr()
+    assert "training: " in res
+    assert "{epochs}/{epochs}".format(epochs=epochs) in res
+    assert "{batches}/{batches}".format(batches=batches) in res
 
-        # just epoch (no batch) progress
+    # auto-detect epochs and batches
-        model.fit(
+    model.fit(
-            x,
+        x,
-            x,
+        x,
-            epochs=epochs,
+        epochs=epochs,
-            batch_size=batch_size,
+        batch_size=batch_size,
-            verbose=False,
+        verbose=False,
-            callbacks=[
+        callbacks=[TqdmCallback(desc="training", verbose=2)],
-                TqdmCallback(
+    )
-                    epochs,
+    _, res = capsys.readouterr()
-                    data_size=len(x),
+    assert "training: " in res
-                    batch_size=batch_size,
+    assert "{epochs}/{epochs}".format(epochs=epochs) in res
-                    verbose=0,
+    assert "{batches}/{batches}".format(batches=batches) in res
-                    tqdm_class=Tqdm,
-                )],
-        )
-        res = our_file.getvalue()
-        assert "{epochs}/{epochs}".format(epochs=epochs) in res
-        assert "{batches}/{batches}".format(batches=batches) not in res
-
-        # full (epoch and batch) progress
-        our_file.seek(0)
-        our_file.truncate()
-        model.fit(
-            x,
-            x,
-            epochs=epochs,
-            batch_size=batch_size,
-            verbose=False,
-            callbacks=[
-                TqdmCallback(
-                    epochs,
-                    data_size=len(x),
-                    batch_size=batch_size,
-                    verbose=2,
-                    tqdm_class=Tqdm,
-                )],
-        )
-        res = our_file.getvalue()
-        assert "{epochs}/{epochs}".format(epochs=epochs) in res
-        assert "{batches}/{batches}".format(batches=batches) in res
-
-        # auto-detect epochs and batches
-        our_file.seek(0)
-        our_file.truncate()
-        model.fit(
-            x,
-            x,
-            epochs=epochs,
-            batch_size=batch_size,
-            verbose=False,
-            callbacks=[TqdmCallback(verbose=2, tqdm_class=Tqdm)],
-        )
-        res = our_file.getvalue()
-        assert "{epochs}/{epochs}".format(epochs=epochs) in res
-        assert "{batches}/{batches}".format(batches=batches) in res