make 'fname' a parameter to 'save' and 'load' only

This simplifies the API by making sure that the filenames are
only provided in one place (the calls to save and load).

Closes #122

adaptive/learner/balancing_learner.py

@@ -317,70 +317,57 @@ class BalancingLearner(BaseLearner):
             learners.append(learner)
         return cls(learners, cdims=arguments)
 
-    def save(self, folder, compress=True):
+    def save(self, fname, compress=True):
         """Save the data of the child learners into pickle files
         in a directory.
 
         Parameters
         ----------
-        folder : str
-            Directory in which the learners's data will be saved.
+        fname: callable
+            Given a learner, returns a filename into which to save the data
         compress : bool, default True
             Compress the data upon saving using `gzip`. When saving
             using compression, one must load it with compression too.
 
-        Notes
-        -----
-        The child learners need to have a 'fname' attribute in order to use
-        this method.
-
         Example
         -------
-        >>> def combo_fname(val):
-        ...     return '__'.join([f'{k}_{v}.p' for k, v in val.items()])
-        ...
-        ... def f(x, a, b): return a * x**2 + b
-        ...
-        >>> learners = []
-        >>> for combo in adaptive.utils.named_product(a=[1, 2], b=[1]):
-        ...     l = Learner1D(functools.partial(f, combo=combo))
-        ...     l.fname = combo_fname(combo)  # 'a_1__b_1.p', 'a_2__b_1.p' etc.
-        ...     learners.append(l)
-        ... learner = BalancingLearner(learners)
-        ... # Run the learner
-        ... runner = adaptive.Runner(learner)
-        ... # Then save
-        ... learner.save('data_folder')  # use 'load' in the same way
+        >>> def combo_fname(learner):
+        ...     val = learner.function.keywords  # because functools.partial
+        ...     fname = '__'.join([f'{k}_{v}.pickle' for k, v in val])
+        ...     return 'data_folder/' + fname
+        >>>
+        >>> def f(x, a, b): return a * x**2 + b
+        >>>
+        >>> learners = [Learner1D(functools.partial(f, **combo), (-1, 1))
+        ...             for combo in adaptive.utils.named_product(a=[1, 2], b=[1]]
+        >>>
+        >>> learner = BalancingLearner(learners)
+        >>> # Run the learner
+        >>> runner = adaptive.Runner(learner)
+        >>> # Then save
+        >>> learner.save(combo_fname)  # use 'load' in the same way
         """
-        if len(self.learners) != len(set(l.fname for l in self.learners)):
-            raise RuntimeError("The 'learner.fname's are not all unique.")
-
         for l in self.learners:
-            l.save(os.path.join(folder, l.fname), compress=compress)
+            l.save(fname(l), compress=compress)
 
-    def load(self, folder, compress=True):
+    def load(self, fname, compress=True):
         """Load the data of the child learners from pickle files
         in a directory.
 
         Parameters
         ----------
-        folder : str
-            Directory from which the learners's data will be loaded.
+        fname: callable
+            Given a learner, returns a filename into which to save the data
         compress : bool, default True
             If the data is compressed when saved, one must load it
             with compression too.
 
-        Notes
-        -----
-        The child learners need to have a 'fname' attribute in order to use
-        this method.
-
         Example
         -------
         See the example in the `BalancingLearner.save` doc-string.
         """
         for l in self.learners:
-            l.load(os.path.join(folder, l.fname), compress=compress)
+            l.load(fname(l), compress=compress)
 
     def _get_data(self):
         return [l._get_data() for l in learner.learners]

adaptive/learner/base_learner.py

@@ -107,48 +107,31 @@ class BaseLearner(metaclass=abc.ABCMeta):
         """
         self._set_data(other._get_data())
 
-    def save(self, fname=None, compress=True):
+    def save(self, fname, compress=True):
         """Save the data of the learner into a pickle file.
 
         Parameters
         ----------
-        fname : str, optional
-            The filename of the learner's pickle data file. If None use
-            the 'fname' attribute, like 'learner.fname = "example.p".
+        fname : str
+            The filename into which to save the learner's data.
         compress : bool, default True
             Compress the data upon saving using 'gzip'. When saving
             using compression, one must load it with compression too.
-
-        Notes
-        -----
-        There are **two ways** of naming the files:
-
-        1. Using the ``fname`` argument in ``learner.save(fname='example.p')``
-        2. Setting the ``fname`` attribute, like
-           ``learner.fname = "data/example.p"`` and then ``learner.save()``.
         """
-        fname = fname or self.fname
         data = self._get_data()
         save(fname, data, compress)
 
-    def load(self, fname=None, compress=True):
+    def load(self, fname, compress=True):
         """Load the data of a learner from a pickle file.
 
         Parameters
         ----------
-        fname : str, optional
-            The filename of the saved learner's pickled data file.
-            If None use the 'fname' attribute, like
-            'learner.fname = "example.p".
+        fname : str
+            The filename from which to load the learner's data.
         compress : bool, default True
             If the data is compressed when saved, one must load it
             with compression too.
-
-        Notes
-        -----
-        See the notes in the `save` doc-string.
         """
-        fname = fname or self.fname
         with suppress(FileNotFoundError, EOFError):
             data = load(fname, compress)
             self._set_data(data)
@@ -158,19 +141,3 @@ class BaseLearner(metaclass=abc.ABCMeta):
 
     def __setstate__(self, state):
         self.__dict__ = state
-
-    @property
-    def fname(self):
-        """Filename for the learner when it is saved (or loaded) using
-        `~adaptive.BaseLearner.save` (or `~adaptive.BaseLearner.load` ).
-        """
-        # This is a property because then it will be availible in the DataSaver
-        try:
-            return self._fname
-        except AttributeError:
-            raise AttributeError("Set 'learner.fname' or use the 'fname'"
-                " argument when using 'learner.save' or 'learner.load'.")
-
-    @fname.setter
-    def fname(self, fname):
-        self._fname = fname

adaptive/learner/data_saver.py

@@ -53,13 +53,13 @@ class DataSaver:
         self.learner._set_data(learner_data)
 
     @copy_docstring_from(BaseLearner.save)
-    def save(self, fname=None, compress=True):
+    def save(self, fname, compress=True):
         # We copy this method because the 'DataSaver' is not a
         # subclass of the 'BaseLearner'.
         BaseLearner.save(self, fname, compress)
 
     @copy_docstring_from(BaseLearner.load)
-    def load(self, fname=None, compress=True):
+    def load(self, fname, compress=True):
         # We copy this method because the 'DataSaver' is not a
         # subclass of the 'BaseLearner'.
         BaseLearner.load(self, fname, compress)

adaptive/tests/test_learners.py

@@ -430,15 +430,15 @@ def test_saving_of_balancing_learner(learner_type, f, learner_kwargs):
     learner = BalancingLearner([learner_type(f, **learner_kwargs)])
     control = BalancingLearner([learner_type(f, **learner_kwargs)])
 
-    # set fnames
-    learner.learners[0].fname = 'test'
-    control.learners[0].fname = 'test'
-
     simple(learner, lambda l: l.learners[0].npoints > 100)
     folder = tempfile.mkdtemp()
+
+    def fname(learner):
+        return folder + 'test'
+
     try:
-        learner.save(folder=folder)
-        control.load(folder=folder)
+        learner.save(fname)
+        control.load(fname)
         if learner_type is not Learner1D:
             # Because different scales result in differnt losses
             np.testing.assert_almost_equal(learner.loss(), control.loss())