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Merge remote-tracking branch 'origin/master' into average1d
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‎CHANGELOG.md‎

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# Changelog
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## [Unreleased](https://github.com/python-adaptive/adaptive/tree/HEAD)
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[Full Changelog](https://github.com/python-adaptive/adaptive/compare/v0.12.2...HEAD)
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**Closed issues:**
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- Cryptic error when importing lmfit [\#314](https://github.com/python-adaptive/adaptive/issues/314)
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## [v0.12.2](https://github.com/python-adaptive/adaptive/tree/v0.12.2) (2021-03-23)
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[Full Changelog](https://github.com/python-adaptive/adaptive/compare/v0.12.1...v0.12.2)
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**Merged pull requests:**
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- raise an AttributeError when attribute doesn't exists, closes \#314 [\#315](https://github.com/python-adaptive/adaptive/pull/315) ([basnijholt](https://github.com/basnijholt))
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## [v0.12.1](https://github.com/python-adaptive/adaptive/tree/v0.12.1) (2021-03-23)
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[Full Changelog](https://github.com/python-adaptive/adaptive/compare/v0.12.0...v0.12.1)

‎README.rst‎

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``adaptive`` is an open-source Python library designed to
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make adaptive parallel function evaluation simple. With ``adaptive`` you
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just supply a function with its bounds, and it will be evaluated at the
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“best” points in parameter space. With just a few lines of code you can
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evaluate functions on a computing cluster, live-plot the data as it
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returns, and fine-tune the adaptive sampling algorithm.
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“best” points in parameter space, rather than unecessarily computing *all* points on a dense grid.
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With just a few lines of code you can evaluate functions on a computing cluster,
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live-plot the data as it returns, and fine-tune the adaptive sampling algorithm.
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``adaptive`` shines on computations where each evaluation of the function
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takes *at least* ≈100ms due to the overhead of picking potentially interesting points.
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Run the ``adaptive`` example notebook `live on
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Binder <https://mybinder.org/v2/gh/python-adaptive/adaptive/master?filepath=example-notebook.ipynb>`_

‎adaptive/learner/average_learner.py‎

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@@ -56,6 +56,10 @@ def __init__(self, function, atol=None, rtol=None, min_npoints=2):
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def n_requested(self):
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return self.npoints + len(self.pending_points)
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def to_numpy(self):
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"""Data as NumPy array of size (npoints, 2) with seeds and values."""
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return np.array(sorted(self.data.items()))
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def ask(self, n, tell_pending=True):
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points = list(range(self.n_requested, self.n_requested + n))
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if n < self.min_npoints:
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return np.inf
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standard_error = self.std / sqrt(n)
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return max(
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standard_error / self.atol, standard_error / abs(self.mean) / self.rtol
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)
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aloss = standard_error / self.atol
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rloss = standard_error / self.rtol
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if self.mean != 0:
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rloss /= abs(self.mean)
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return max(aloss, rloss)
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def _loss_improvement(self, n):
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loss = self.loss()

‎adaptive/learner/integrator_coeffs.py‎

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@@ -186,5 +186,8 @@ def _coefficients():
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return locals()
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def __getattr__(attr):
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return _coefficients()[attr]
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def __getattr__(name):
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try:
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return _coefficients()[name]
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except KeyError:
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raise AttributeError(f"module {__name__} has no attribute {name}")

‎adaptive/learner/integrator_learner.py‎

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xs, ys = zip(*[(x, y) for ival in ivals for x, y in sorted(ival.data.items())])
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return hv.Path((xs, ys))
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def to_numpy(self):
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"""Data as NumPy array of size (npoints, 2)."""
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return np.array(sorted(self.data.items()))
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def _get_data(self):
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# Change the defaultdict of SortedSets to a normal dict of sets.
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x_mapping = {k: set(v) for k, v in self.x_mapping.items()}

‎adaptive/learner/learner1D.py‎

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... return x**2
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>>>
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>>> loss = resolution_loss_function(min_length=0.01, max_length=1)
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>>> learner = adaptive.Learner1D(f, bounds=[(-1, -1), (1, 1)], loss_per_triangle=loss)
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>>> learner = adaptive.Learner1D(f, bounds=(-1, -1), loss_per_interval=loss)
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"""
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@uses_nth_neighbors(0)
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return 1
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return self._vdim
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def to_numpy(self):
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"""Data as NumPy array of size ``(npoints, 2)`` if ``learner.function`` returns a scalar
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and ``(npoints, 1+vdim)`` if ``learner.function`` returns a vector of length ``vdim``."""
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return np.array([(x, *np.atleast_1d(y)) for x, y in sorted(self.data.items())])
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@property
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def npoints(self):
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"""Number of evaluated points."""

‎adaptive/learner/learner2D.py‎

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else:
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return np.array([xy_scale[0], xy_scale[1] / self.aspect_ratio])
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def to_numpy(self):
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"""Data as NumPy array of size ``(npoints, 3)`` if ``learner.function`` returns a scalar
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and ``(npoints, 2+vdim)`` if ``learner.function`` returns a vector of length ``vdim``."""
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return np.array(
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[(x, y, *np.atleast_1d(z)) for (x, y), z in sorted(self.data.items())]
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)
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def _scale(self, points):
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points = np.asarray(points, dtype=float)
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return (points - self.xy_mean) / self.xy_scale

‎adaptive/learner/learnerND.py‎

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self._vdim = 1
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return self._vdim if self._vdim is not None else 1
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def to_numpy(self):
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"""Data as NumPy array of size ``(npoints, dim+vdim)``, where ``dim`` is the
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size of the input dimension and ``vdim`` is the length of the return value
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of ``learner.function``."""
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return np.array([(*p, *np.atleast_1d(v)) for p, v in sorted(self.data.items())])
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@property
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def bounds_are_done(self):
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return all(p in self.data for p in self._bounds_points)

‎adaptive/runner.py‎

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remaining = self._remove_unfinished()
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if remaining:
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concurrent.wait(remaining)
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# Some futures get their result set, despite being cancelled.
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# see https://github.com/python-adaptive/adaptive/issues/319
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with_result = [f for f in remaining if not f.cancelled() and f.done()]
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self._process_futures(with_result)
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self._cleanup()
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def elapsed_time(self):

‎adaptive/tests/test_average_learner.py‎

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)
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simple(learner, lambda l: l.loss() < 1)
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assert learner.npoints >= max(2, min_npoints)
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def test_zero_mean():
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# see https://github.com/python-adaptive/adaptive/issues/275
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learner = AverageLearner(None, rtol=0.01)
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learner.tell(0, -1)
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learner.tell(1, 1)
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learner.loss()

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