Merge pull request #593 from ICB-DCM/develop

Release 0.12.9

Author: yannikschaelte

.pre-commit-config.yaml

@@ -11,12 +11,12 @@
 
 repos:
 - repo: https://github.com/psf/black
-  rev: 22.3.0
+  rev: 23.1.0
   hooks:
   - id: black
     description: The uncompromising code formatter
 - repo: https://github.com/pycqa/isort
-  rev: 5.10.1
+  rev: 5.12.0
   hooks:
   - id: isort
     name: isort (python)
@@ -27,14 +27,14 @@ repos:
     name: isort (pyi)
     types: [pyi]
 - repo: https://github.com/nbQA-dev/nbQA
-  rev: 1.2.3
+  rev: 1.6.1
   hooks:
     - id: nbqa-black
     - id: nbqa-pyupgrade
       args: [--py36-plus]
     - id: nbqa-isort
 - repo: https://github.com/pre-commit/pre-commit-hooks
-  rev: v4.2.0
+  rev: v4.4.0
   hooks:
   - id: check-yaml
     description: Check yaml files for parseable syntax
@@ -55,6 +55,6 @@ repos:
   - id: style
     name: Check style
     description: Check style
-    entry: tox -e project,flake8
-    language: python
+    entry: tox -e flake8 --
+    language: system
     types: [python]

CHANGELOG.rst

@@ -7,6 +7,15 @@ Release Notes
 0.12 Series
 ...........
 
+0.12.9 (2023-03-01)
+-------------------
+
+Minor:
+
+* Improve documentation of p-norm (#592)
+* Update citation to JOSS
+* Temporarily fixate sqlalchemy version
+* Update pre-commit hooks (all #596)
 
 0.12.8 (2022-11-16)
 -------------------

CITE.rst

@@ -5,21 +5,26 @@ When using pyABC version >= 0.8, please cite:
 
 * Schälte, Y., Klinger, E., Alamoudi, E., Hasenauer, J., 2022.
   pyABC: Efficient and robust easy-to-use approximate Bayesian computation.
-  arXiv.
-  https://doi.org/10.48550/arxiv.2203.13043.
+  Journal of Open Source Software.
+  https://doi.org/10.21105/joss.04304.
 
   .. code-block:: bibtex
 
-     @article{schaelte2022pyabc,
-       title = {pyABC: Efficient and robust easy-to-use approximate Bayesian computation},
-       author = {Schälte, Yannik and Klinger, Emmanuel and Alamoudi, Emad and Hasenauer, Jan},
-       journal = {arXiv},
-       year = {2022},
-       doi = {10.48550/arxiv.2203.13043},
-       url = {https://arxiv.org/abs/2203.13043},
-     }
-
-When using pyABC version < 0.8 or functionality not introduced in later versions, please cite:
+    @article{schaelte2022pyabc,
+      title = {pyABC: Efficient and robust easy-to-use approximate Bayesian computation},
+      author = {Schälte, Yannik and Klinger, Emmanuel and Alamoudi, Emad and Hasenauer, Jan},
+      journal = {Journal of Open Source Software},
+      publisher = {The Open Journal},
+      year = {2022},
+      volume = {7},
+      number = {74},
+      pages = {4304},
+      doi = {10.21105/joss.04304},
+      url = {https://doi.org/10.21105/joss.04304},
+    }
+
+When using pyABC version < 0.8 or functionality not introduced in later
+versions, please (also) cite:
 
 * Klinger, E., Rickert, D., Hasenauer, J., 2018.
   pyABC: distributed, likelihood-free inference.

doc/examples/adaptive_distances.ipynb

@@ -111,6 +111,7 @@
     "df_logger = logging.getLogger(\"ABC.Distance\")\n",
     "df_logger.setLevel(logging.DEBUG)\n",
     "\n",
+    "\n",
     "# model definition\n",
     "def model(p):\n",
     "    return {\n",
@@ -470,6 +471,7 @@
     "df_logger = logging.getLogger(\"Distance\")\n",
     "df_logger.setLevel(logging.DEBUG)\n",
     "\n",
+    "\n",
     "# model definition\n",
     "def model(p):\n",
     "    return {\n",

pyabc/distance/pnorm.py

@@ -35,8 +35,8 @@ class PNormDistance(Distance):
         \\left [\\sum_{i} \\left| w_i ( x_i-y_i ) \\right|^{p} \\right ]^{1/p}
 
     E.g.
-    * p=1 for a Euclidean or L1 metric,
-    * p=2 for a Manhattan or L2 metric,
+    * p=1 for a Manhattan or L1 metric,
+    * p=2 for a Euclidean or L2 metric,
     * p=np.inf for a Chebyshev, maximum or inf metric.
 
     Parameters

pyabc/epsilon/temperature.py

@@ -372,6 +372,7 @@ def match_acceptance_rate(weights, pds, pdf_norm, kernel_scale, target_rate):
     For a temperature close to 1, subtler changes are neccesary, however here
     the logarhtm is nearly linear anyway.
     """
+
     # objective function which we wish to find a root for
     def obj(b):
         beta = np.exp(b)

pyabc/external/r/r_rpy2.py

@@ -78,7 +78,10 @@ class R:
     def __init__(self, source_file: str):
         if r is None:
             raise ImportError("Install rpy2, e.g. via `pip install pyabc[R]`")
-        warnings.warn("The support of R via rpy2 is considered experimental.")
+        warnings.warn(
+            "The support of R via rpy2 is considered experimental.",
+            stacklevel=2,
+        )
         self.source_file = source_file
         self._read_source()
 

pyabc/population.py

@@ -72,7 +72,6 @@ def __init__(
         proposal_id: int = 0,
         preliminary: bool = False,
     ):
-
         self.m = m
         self.parameter = parameter
         self.weight = weight

pyabc/sampler/multicore_evaluation_parallel.py

@@ -43,7 +43,6 @@ def work(
         sample.append(new_sim)
 
         if new_sim.accepted:
-
             # increase number of accepted particles
             with n_acc.get_lock():
                 n_acc.value += 1

pyabc/sge/sge.py

@@ -133,7 +133,6 @@ def __init__(
         execution_context=DefaultContext,
         chunk_size=1,
     ):
-
         # simple assignments
         self.memory = memory
         self.time_h = time_h
@@ -159,7 +158,8 @@ def __init__(
         self.job_name = name
         if self.config["SGE"]["PRIORITY"] == "0":
             warnings.warn(
-                "Priority set to 0. " "This enables the reservation flag."
+                "Priority set to 0. " "This enables the reservation flag.",
+                stacklevel=2,
             )
         self.num_threads = num_threads
         self.execution_context = execution_context
@@ -168,7 +168,8 @@ def __init__(
         if chunk_size != 1:
             warnings.warn(
                 "Chunk size != 1. "
-                "This can potentially have bad side effect."
+                "This can potentially have bad side effect.",
+                stacklevel=2,
             )
 
         if not sge_available():

pyabc/storage/dataframe_bytes_storage.py

@@ -129,6 +129,7 @@ def df_to_bytes(df: pd.DataFrame) -> bytes:
             "Can't find pyarrow, falling back to less efficient csv "
             "to store pandas DataFrames.\n"
             "Install e.g. via `pip install pyabc[pyarrow]`",
+            stacklevel=2,
         )
         return df_to_bytes_csv(df)
     return df_to_bytes_parquet(df)

pyabc/storage/history.py

@@ -4,7 +4,7 @@
 import os
 import tempfile
 from functools import wraps
-from typing import List, Union
+from typing import List, Tuple, Union
 
 import numpy as np
 import pandas as pd
@@ -308,7 +308,7 @@ def alive_models(self, t: int = None) -> List:
     @with_session
     def get_distribution(
         self, m: int = 0, t: int = None
-    ) -> (pd.DataFrame, np.ndarray):
+    ) -> Tuple[pd.DataFrame, np.ndarray]:
         """
         Returns the weighted population sample for model m and timepoint t
         as a tuple.
@@ -997,7 +997,7 @@ def n_populations(self):
     @with_session
     def get_weighted_sum_stats_for_model(
         self, m: int = 0, t: int = None
-    ) -> (np.ndarray, List):
+    ) -> Tuple[np.ndarray, List]:
         """
         Summary statistics for model `m`. The weights sum to 1, unless
         there were multiple acceptances per particle.
@@ -1046,7 +1046,7 @@ def get_weighted_sum_stats_for_model(
     @with_session
     def get_weighted_sum_stats(
         self, t: int = None
-    ) -> (List[float], List[dict]):
+    ) -> Tuple[List[float], List[dict]]:
         """
         Population's weighted summary statistics.
         These weights do not necessarily sum up to 1.

pyabc/transition/grid_search.py

@@ -40,7 +40,6 @@ def __init__(
         error_score='raise',
         return_train_score=True,
     ):
-
         if estimator is None:
             estimator = MultivariateNormalTransition()
         if param_grid is None:

pyabc/version.py

@@ -1 +1 @@
-__version__ = '0.12.8'
+__version__ = '0.12.9'

setup.cfg

@@ -60,7 +60,8 @@ install_requires =
     redis >= 2.10.6
     distributed >= 2022.10.2
     matplotlib >= 3.3.0
-    sqlalchemy >= 1.3.18
+    # until https://github.com/pandas-dev/pandas/issues/40686
+    sqlalchemy >= 1.3.18, < 2.0.0
     jabbar >= 0.0.10
     gitpython >= 3.1.7
 

test/base/test_epsilon.py

@@ -20,7 +20,7 @@ def test_constantepsilon():
 
 def test_listepsilon():
     eps = pyabc.ListEpsilon([3.5, 2.3, 1, 0.3])
-    with pytest.raises(Exception):
+    with pytest.raises(IndexError):
         eps(4)
 
 

test/base/test_storage.py

@@ -572,6 +572,7 @@ def test_export():
 
     Just calls export and does some very basic checks.
     """
+
     # simple problem
     def model(p):
         return {"y": p["p"] + 0.1 * np.random.normal()}