From fcdb05321d469343021152f3859cff8e8d19ab4e Mon Sep 17 00:00:00 2001
From: Joseph Weston <joseph@weston.cloud>
Date: Mon, 19 Nov 2018 18:16:46 +0100
Subject: [PATCH] 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 | 59 +++++++++++----------------
adaptive/learner/base_learner.py | 45 +++-----------------
adaptive/learner/data_saver.py | 4 +-
adaptive/tests/test_learners.py | 12 +++---
4 files changed, 37 insertions(+), 83 deletions(-)
diff --git a/adaptive/learner/balancing_learner.py b/adaptive/learner/balancing_learner.py
index f8ec0b08..65fc192a 100644
--- a/adaptive/learner/balancing_learner.py
+++ b/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]
diff --git a/adaptive/learner/base_learner.py b/adaptive/learner/base_learner.py
index eed2c5a8..d7c97382 100644
--- a/adaptive/learner/base_learner.py
+++ b/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
diff --git a/adaptive/learner/data_saver.py b/adaptive/learner/data_saver.py
index 8e035994..78a9e31b 100644
--- a/adaptive/learner/data_saver.py
+++ b/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)
diff --git a/adaptive/tests/test_learners.py b/adaptive/tests/test_learners.py
index a518f264..ed4c69a0 100644
--- a/adaptive/tests/test_learners.py
+++ b/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())
--
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