Merge remote-tracking branch 'origin/master' into mypy

Author: basnijholt

.github/workflows/automerge.yml

@@ -0,0 +1,27 @@
+name: automerge
+on:
+  pull_request:
+    types:
+      - labeled
+      - unlabeled
+      - synchronize
+      - opened
+      - edited
+      - ready_for_review
+      - reopened
+      - unlocked
+  pull_request_review:
+    types:
+      - submitted
+  check_suite:
+    types:
+      - completed
+  status: {}
+jobs:
+  automerge:
+    runs-on: ubuntu-latest
+    steps:
+      - name: automerge
+        uses: "pascalgn/automerge-action@f81beb99aef41bb55ad072857d43073fba833a98"
+        env:
+          GITHUB_TOKEN: "${{ secrets.GITHUB_TOKEN }}"

.gitignore

@@ -95,3 +95,6 @@ ENV/
 benchmarks/html/
 benchmarks/results/
 .asv
+
+# vscode configuration
+.vscode/

.pre-commit-config.yaml

@@ -1,32 +1,27 @@
 repos:
--   repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v2.4.0
-    hooks:
-    -   id: trailing-whitespace
-    -   id: end-of-file-fixer
-    -   id: check-docstring-first
-    -   id: check-yaml
-    -   id: debug-statements
-    -   id: check-ast
--   repo: https://github.com/ambv/black
-    rev: 19.10b0
-    hooks:
-    - id: black
-      language_version: python3.7
--   repo: https://github.com/asottile/pyupgrade
-    rev: v1.25.2
-    hooks:
-    -   id: pyupgrade
-        args: ['--py36-plus']
--   repo: https://github.com/asottile/seed-isort-config
-    rev: v1.9.3
-    hooks:
-    -   id: seed-isort-config
--   repo: https://github.com/pre-commit/mirrors-isort
-    rev: v4.3.21
-    hooks:
-    -   id: isort
--   repo: https://gitlab.com/pycqa/flake8
-    rev: 3.7.9
-    hooks:
-    -   id: flake8
+- repo: https://github.com/pre-commit/pre-commit-hooks
+  rev: v3.3.0
+  hooks:
+  - id: trailing-whitespace
+  - id: end-of-file-fixer
+  - id: check-docstring-first
+  - id: check-yaml
+  - id: debug-statements
+  - id: check-ast
+- repo: https://github.com/ambv/black
+  rev: 20.8b1
+  hooks:
+  - id: black
+- repo: https://github.com/asottile/pyupgrade
+  rev: v2.7.3
+  hooks:
+  - id: pyupgrade
+    args: ['--py36-plus']
+- repo: https://github.com/timothycrosley/isort
+  rev: 5.6.4
+  hooks:
+  - id: isort
+- repo: https://gitlab.com/pycqa/flake8
+  rev: 3.8.4
+  hooks:
+  - id: flake8

.zenodo.json

@@ -0,0 +1,35 @@
+{
+  "creators": [
+    {
+      "affiliation": "Microsoft, Station Q",
+      "name": "Nijholt, Bas",
+      "orcid": "0000-0003-0383-4986"
+    },
+    {
+      "affiliation": "Microsoft, Station Q",
+      "name": "Weston, Joseph",
+      "orcid": "0000-0002-7014-7134"
+    },
+    {
+      "affiliation": "Kavli Institute of Nanoscience, Delft University of Technology",
+      "name": "Hoofwijk, Jorn"
+    },
+    {
+      "affiliation": "Kavli Institute of Nanoscience, Delft University of Technology",
+      "name": "Akhmerov, Anton",
+      "orcid": "0000-0001-8031-1340"
+    }
+  ],
+  "keywords": [
+    "adaptive-learning",
+    "machine-learning",
+    "python",
+    "live-plots",
+    "parallel-computing",
+    "adaptive-sampling",
+    "parallel",
+    "adaptive active-learning"
+  ],
+  "license": "BSD-3-Clause",
+  "upload_type": "software"
+}

AUTHORS.md

@@ -11,4 +11,6 @@ Other contributors to Adaptive include:
 + Andrey E. Antipov
 + [Christoph Groth](<http://inac.cea.fr/Pisp/christoph.groth/>)
 + Jorn Hoofwijk
++ Philippe Solodov (@philippeitis)
 + Victor Negîrneac (@caenrigen)
++ Thomas A Caswell (@tacaswell)

LICENSE

@@ -1,6 +1,6 @@
 BSD 3-Clause License
 
-Copyright (c) 2017-2019, Adaptive authors
+Copyright (c) 2017-2020, Adaptive authors
 All rights reserved.
 
 Redistribution and use in source and binary forms, with or without

README.rst

@@ -55,12 +55,30 @@ In addition to the learners, ``adaptive`` also provides primitives for
 running the sampling across several cores and even several machines,
 with built-in support for
 `concurrent.futures <https://docs.python.org/3/library/concurrent.futures.html>`_,
+`mpi4py <https://mpi4py.readthedocs.io/en/stable/mpi4py.futures.html>`_,
+`loky <https://loky.readthedocs.io/en/stable/>`_,
 `ipyparallel <https://ipyparallel.readthedocs.io/en/latest/>`_ and
 `distributed <https://distributed.readthedocs.io/en/latest/>`_.
 
 Examples
 --------
 
+Adaptively learning a 1D function (the `gif` below) and live-plotting the process in a Jupyter notebook is as easy as
+
+.. code:: python
+
+    from adaptive import notebook_extension, Runner, Learner1D
+    notebook_extension()
+
+    def peak(x, a=0.01):
+        return x + a**2 / (a**2 + x**2)
+
+    learner = Learner1D(peak, bounds=(-1, 1))
+    runner = Runner(learner, goal=lambda l: l.loss() < 0.01)
+    runner.live_info()
+    runner.live_plot()
+
+
 .. raw:: html
 
   <img src="https://user-images.githubusercontent.com/6897215/38739170-6ac7c014-3f34-11e8-9e8f-93b3a3a3d61b.gif" width='20%'> </img> <img src="https://user-images.githubusercontent.com/6897215/35219611-ac8b2122-ff73-11e7-9332-adffab64a8ce.gif" width='40%'> </img> <img src="https://user-images.githubusercontent.com/6897215/47256441-d6d53700-d480-11e8-8224-d1cc49dbdcf5.gif" width='20%'> </img>
@@ -89,6 +107,13 @@ The recommended way to install adaptive is using ``conda``:
 The ``[notebook]`` above will also install the optional dependencies for
 running ``adaptive`` inside a Jupyter notebook.
 
+To use Adaptive in Jupyterlab, you need to install the following labextensions.
+
+.. code:: bash
+
+    jupyter labextension install @jupyter-widgets/jupyterlab-manager
+    jupyter labextension install @pyviz/jupyterlab_pyviz
+
 Development
 -----------
 

adaptive/_version.py

@@ -3,9 +3,9 @@
 import os
 import subprocess
 from collections import namedtuple
-from distutils.command.build_py import build_py as build_py_orig
 from typing import Dict
 
+from setuptools.command.build_py import build_py as build_py_orig
 from setuptools.command.sdist import sdist as sdist_orig
 
 Version = namedtuple("Version", ("release", "dev", "labels"))
@@ -16,6 +16,13 @@
 package_root = os.path.dirname(os.path.realpath(__file__))
 package_name = os.path.basename(package_root)
 distr_root = os.path.dirname(package_root)
+# If the package is inside a "src" directory the
+# distribution root is 1 level up.
+if os.path.split(distr_root)[1] == "src":
+    _package_root_inside_src = True
+    distr_root = os.path.dirname(distr_root)
+else:
+    _package_root_inside_src = False
 
 STATIC_VERSION_FILE = "_static_version.py"
 
@@ -190,7 +197,11 @@ def run(self):
 class _sdist(sdist_orig):
     def make_release_tree(self, base_dir, files):
         super().make_release_tree(base_dir, files)
-        _write_version(os.path.join(base_dir, package_name, STATIC_VERSION_FILE))
+        if _package_root_inside_src:
+            p = os.path.join("src", package_name)
+        else:
+            p = package_name
+        _write_version(os.path.join(base_dir, p, STATIC_VERSION_FILE))
 
 
 cmdclass = dict(sdist=_sdist, build_py=_build_py)

adaptive/learner/average_learner.py

@@ -20,6 +20,8 @@ class AverageLearner(BaseLearner):
         Desired absolute tolerance.
     rtol : float
         Desired relative tolerance.
+    min_npoints : int
+        Minimum number of points to sample.
 
     Attributes
     ----------
@@ -36,6 +38,7 @@ def __init__(
         function: Callable,
         atol: Optional[float] = None,
         rtol: Optional[float] = None,
+        min_npoints: int = 2,
     ) -> None:
         if atol is None and rtol is None:
             raise Exception("At least one of `atol` and `rtol` should be set.")
@@ -50,6 +53,8 @@ def __init__(
         self.atol = atol
         self.rtol = rtol
         self.npoints = 0
+        # Cannot estimate standard deviation with fewer than 2 points.
+        self.min_npoints = max(min_npoints, 2)
         self.sum_f = 0
         self.sum_f_sq = 0
 
@@ -98,7 +103,7 @@ def std(self) -> float:
         """The corrected sample standard deviation of the values
         in `data`."""
         n = self.npoints
-        if n < 2:
+        if n < self.min_npoints:
             return np.inf
         numerator = self.sum_f_sq - n * self.mean ** 2
         if numerator < 0:
@@ -112,7 +117,7 @@ def loss(self, real: bool = True, *, n=None) -> float:
             n = self.npoints if real else self.n_requested
         else:
             n = n
-        if n < 2:
+        if n < self.min_npoints:
             return np.inf
         standard_error = self.std / sqrt(n)
         return max(
@@ -143,10 +148,24 @@ def plot(self):
             return hv.Histogram([[], []])
         num_bins = int(max(5, sqrt(self.npoints)))
         vals = hv.Points(vals)
-        return hv.operation.histogram(vals, num_bins=num_bins, dimension=1)
+        return hv.operation.histogram(vals, num_bins=num_bins, dimension="y")
 
     def _get_data(self) -> Tuple[Dict[int, float], int, float, float]:
         return (self.data, self.npoints, self.sum_f, self.sum_f_sq)
 
     def _set_data(self, data: Tuple[Dict[int, float], int, float, float]) -> None:
         self.data, self.npoints, self.sum_f, self.sum_f_sq = data
+
+    def __getstate__(self):
+        return (
+            self.function,
+            self.atol,
+            self.rtol,
+            self.min_npoints,
+            self._get_data(),
+        )
+
+    def __setstate__(self, state):
+        function, atol, rtol, min_npoints, data = state
+        self.__init__(function, atol, rtol, min_npoints)
+        self._set_data(data)

adaptive/learner/balancing_learner.py

@@ -455,3 +455,14 @@ def _get_data(self):
     def _set_data(self, data):
         for l, _data in zip(self.learners, data):
             l._set_data(_data)
+
+    def __getstate__(self):
+        return (
+            self.learners,
+            self._cdims_default,
+            self.strategy,
+        )
+
+    def __setstate__(self, state):
+        learners, cdims, strategy = state
+        self.__init__(learners, cdims=cdims, strategy=strategy)