diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 27bea94..276067b 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -1,17 +1,14 @@ repos: -- repo: https://github.com/pre-commit/mirrors-isort - rev: 'v4.3.16' - hooks: - - id: isort - additional_dependencies: ['future-fstrings==1.0.0', 'isort[pyproject]'] -- repo: https://github.com/ambv/black - rev: stable - hooks: - - id: black - language_version: python3.6 - additional_dependencies: ['future-fstrings==1.0.0'] -- repo: https://github.com/pre-commit/mirrors-mypy - rev: 'v0.730' - hooks: - - id: mypy - additional_dependencies: ['future-fstrings==1.0.0'] + - repo: https://github.com/pre-commit/mirrors-isort + rev: "v5.10.1" + hooks: + - id: isort + - repo: https://github.com/ambv/black + rev: stable + hooks: + - id: black + language_version: python3.7 + - repo: https://github.com/pre-commit/mirrors-mypy + rev: "v0.960" + hooks: + - id: mypy diff --git a/.travis.yml b/.travis.yml index 4e18184..3f18850 100644 --- a/.travis.yml +++ b/.travis.yml @@ -1,11 +1,9 @@ sudo: required language: python python: - # - "3.5" - - "3.6" - - "3.7" + - "3.7.13" - "3.8" -os: linux +os: linux dist: xenial cache: pip services: diff --git a/.vscode/settings.json b/.vscode/settings.json index 98d7e22..36af35f 100644 --- a/.vscode/settings.json +++ b/.vscode/settings.json @@ -4,5 +4,6 @@ "python.testing.unittestEnabled": false, "python.testing.nosetestsEnabled": false, "python.testing.pytestEnabled": true, - "restructuredtext.confPath": "${workspaceFolder}/.tox/py36/lib/python3.6/site-packages/importlib_resources/docs" + "restructuredtext.confPath": "${workspaceFolder}/.tox/py36/lib/python3.7/site-packages/importlib_resources/docs", + "esbonio.sphinx.confDir": "${workspaceFolder}/.tox/py36/lib/python3.7/site-packages/importlib_resources/docs" } \ No newline at end of file diff --git a/Dockerfile b/Dockerfile index b1ff106..f1eced8 100644 --- a/Dockerfile +++ b/Dockerfile @@ -23,8 +23,8 @@ RUN groupadd --gid $USER_GID $USERNAME \ # Install git, process tools, lsb-release (common in install instructions for CLIs) RUN apt -y install git procps lsb-release ffmpeg vim exuberant-ctags zsh wget -# Install any missing dependencies for enhanced language service -RUN apt install -y libicu[0-9][0-9] +# # Install any missing dependencies for enhanced language service +# RUN apt install -y libicu[0-9][0-9] # Clean up RUN apt-get autoremove -y \ @@ -46,7 +46,7 @@ RUN mkdir -p ${HOME}/.ssh \ WORKDIR ${HOME}/workspace # Install Python dependencies from requirements.txt if it exists -RUN conda create -n env python=3.6 +RUN conda create -n env python=3.7 RUN echo "source activate env" > ~/.bashrc ENV PATH /opt/conda/envs/env/bin:$PATH RUN conda install virtualenv diff --git a/README.rst b/README.rst index d7e57fa..d071dcd 100644 --- a/README.rst +++ b/README.rst @@ -83,7 +83,7 @@ Alternatively, you can use conda to set up your local development environment. :: - $ conda create -n incense-dev python=3.6 + $ conda create -n incense-dev python=3.7 $ conda activate incense-dev # virtualenv is required for the precommit environments. $ conda install virtualenv diff --git a/binder/environment.yml b/binder/environment.yml index ba64b59..6080780 100644 --- a/binder/environment.yml +++ b/binder/environment.yml @@ -3,7 +3,7 @@ channels: - conda-forge - anaconda dependencies: - - python=3.6 + - python=3.7 - numpy - matplotlib - pandas @@ -12,5 +12,5 @@ dependencies: - mongodb - seaborn - pip: - - git+https://github.com/IDSIA/sacred.git - - incense + - git+https://github.com/IDSIA/sacred.git + - incense diff --git a/delete_db.py b/delete_db.py index 2d55434..e8fc4d3 100644 --- a/delete_db.py +++ b/delete_db.py @@ -1,14 +1,18 @@ import os +import shutil from pathlib import Path from dotenv import load_dotenv -from pymongo import MongoClient +from pymongo.mongo_client import MongoClient env_path = Path(".") / "infrastructure" / "sacred_setup" / ".env" load_dotenv(dotenv_path=env_path) DB_NAME = os.environ["MONGO_DATABASE"] -client = MongoClient(host="mongo") +client: MongoClient = MongoClient(host="mongo") client.drop_database(DB_NAME) + +shutil.rmtree(Path("~/data/incense_test/").expanduser(), ignore_errors=True) + print(f"Database {DB_NAME} successfully deleted.") diff --git a/demo.ipynb b/demo.ipynb index 3d0bcfb..afd2fe6 100755 --- a/demo.ipynb +++ b/demo.ipynb @@ -550,7 +550,7 @@ "outputs": [ { "data": { - "image/png": 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+ "image/png": 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"text/plain": [ "" ] @@ -907,7 +907,7 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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", 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\n", + "image/png": 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", "text/plain": [ "
" ] @@ -1382,7 +1382,7 @@ "metadata": {}, "outputs": [ { - "name": "stdin", + "name": "stdout", "output_type": "stream", "text": [ "Are you sure you want to delete Experiment(id=2, name=example)? [y/N] N\n" @@ -1403,8 +1403,11 @@ } ], "metadata": { + "interpreter": { + "hash": "06217459e5ebcd87f7dbc73abff955c789972eb225a59b85b696bf601b5ed8ce" + }, "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3.7.13 ('env')", "language": "python", "name": "python3" }, @@ -1418,7 +1421,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.7.13" } }, "nbformat": 4, diff --git a/incense/artifact.py b/incense/artifact.py index 51fbdbd..7b3b280 100755 --- a/incense/artifact.py +++ b/incense/artifact.py @@ -1,4 +1,3 @@ -# -*- coding: future_fstrings -*- import os import pickle import warnings diff --git a/incense/experiment.py b/incense/experiment.py index 4ca65b9..f209239 100755 --- a/incense/experiment.py +++ b/incense/experiment.py @@ -1,13 +1,13 @@ -# -*- coding: future_fstrings -*- import json from pathlib import Path from typing import * import jsonpickle import pandas as pd -from incense.artifact import Artifact, content_type_to_artifact_cls from pyrsistent import freeze, thaw +from incense.artifact import Artifact, content_type_to_artifact_cls + class Experiment: def __init__(self, id_, database, grid_filesystem, data, artifact_links, loader): diff --git a/incense/experiment_loader.py b/incense/experiment_loader.py index bb8f043..d70c907 100755 --- a/incense/experiment_loader.py +++ b/incense/experiment_loader.py @@ -1,13 +1,12 @@ -# -*- coding: future_fstrings -*- import importlib import numbers -from functools import _lru_cache_wrapper, lru_cache +from functools import lru_cache from pathlib import Path from typing import * import gridfs -import pymongo -from pymongo import MongoClient +from pymongo import DESCENDING +from pymongo.mongo_client import MongoClient from .experiment import Experiment, FileSystemExperiment from .query_set import QuerySet @@ -19,7 +18,7 @@ class ExperimentLoader: """Loads artifacts related to experiments.""" def __init__(self, mongo_uri=None, db_name="sacred", unpickle: bool = True): - client = MongoClient(mongo_uri) + client: MongoClient = MongoClient(mongo_uri) self._database = client[db_name] self._runs = self._database.runs self._grid_filesystem = gridfs.GridFS(self._database) @@ -140,7 +139,7 @@ def find_latest(self, n: int = 1, attr: str = "start_time") -> Union[Experiment, Returns: Either the latest experiment or the set of latest experiments in case more than one were requested. """ - cursor = self._runs.find().sort(attr, pymongo.DESCENDING).limit(n) + cursor = self._runs.find().sort(attr, DESCENDING).limit(n) experiments = [self._make_experiment(experiment) for experiment in cursor] if len(experiments) == 1: return experiments[0] @@ -189,6 +188,8 @@ def _search_collection(self, key, value): cursor = self._runs.find({key: {"$regex": rf"{value}"}}) elif isinstance(value, numbers.Real): cursor = self._runs.find({key: value}) + else: + raise ValueError(f"Search value should be either string or number, but was {value}") return cursor diff --git a/incense/query_set.py b/incense/query_set.py index fd2d59b..6afb73c 100644 --- a/incense/query_set.py +++ b/incense/query_set.py @@ -1,4 +1,3 @@ -# -*- coding: future_fstrings -*- from collections import OrderedDict, UserList, defaultdict from concurrent.futures import ThreadPoolExecutor from copy import copy @@ -31,8 +30,8 @@ def project( values in the experiment model can be used inplace of a path. This is useful for summarizing metrics. rename: The renaming strategy used to create the column names. Either "last" to take the last element in each path as a column name or None to use the complete paths as column names. - on_missing: {"raise", "ignore"} Whether to raise an error when missing value is encountered or replace it with - a missing value. + on_missing: {"raise", "ignore"} Whether to raise an error when missing value is encountered or replace it with + a missing value. Returns ------- diff --git a/incense/utils.py b/incense/utils.py index 2c57920..2295cab 100755 --- a/incense/utils.py +++ b/incense/utils.py @@ -1,4 +1,3 @@ -# -*- coding: future_fstrings -*- from functools import reduce from operator import and_, or_ from typing import * diff --git a/mypy.ini b/mypy.ini index 26aeb63..2b95b6b 100644 --- a/mypy.ini +++ b/mypy.ini @@ -1,5 +1,5 @@ [mypy] -python_version = 3.6 +python_version = 3.7 ignore_missing_imports = True [mypy-versioneer.py] diff --git a/pyproject.toml b/pyproject.toml index feab7ed..47a1d69 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.black] line-length = 120 -target-version = ['py36'] +target-version = ['py37'] include = '\.pyi?$' exclude = ''' ( diff --git a/requirements-dev.txt b/requirements-dev.txt index 62cb8ca..8382c98 100644 --- a/requirements-dev.txt +++ b/requirements-dev.txt @@ -1,16 +1,14 @@ -r requirements.txt -pytest -pytest-cov -codecov -tensorflow==2.5.3 -python-dotenv -scikit-learn -jupyterlab -seaborn -pre-commit -tokenize-rt==2.2.0 -pylint -black -doc8 -rope -mypy==0.780 +pytest==7.1.2 +pytest-cov==3.0.0 +codecov==2.1.12 +tensorflow==2.9.1 +python-dotenv==0.20.0 +scikit-learn==1.0.2 +jupyterlab==3.4.2 +seaborn==0.11.2 +pre-commit==2.19.0 +black==22.3.0 +doc8==0.11.2 +rope==1.1.1 +mypy==0.960 diff --git a/requirements.txt b/requirements.txt index e361a5b..f26a338 100755 --- a/requirements.txt +++ b/requirements.txt @@ -1,7 +1,6 @@ -sacred>=0.8 -pandas>=0.23 -pymongo>=3.7 -easydict>=1.9 -ipython>=7.2 -future-fstrings==1.0.0 +sacred==0.8.2 +pandas==1.3.5 +pymongo==4.1.1 +ipython==7.33.0 +pyrsistent==0.18.1 attrs==19.3.0 diff --git a/setup.py b/setup.py index a38b74b..88bdae0 100644 --- a/setup.py +++ b/setup.py @@ -29,15 +29,11 @@ "jupyterlab>=1.0", "pymongo>=3.7", "pyrsistent>=0.15.2", - "future-fstrings==1.0.0", - "tokenize-rt==2.2.0", ], include_package_data=True, classifiers=[ "Development Status :: 3 - Alpha", "Intended Audience :: Science/Research", - "Programming Language :: Python :: 3.5", - "Programming Language :: Python :: 3.6", "Programming Language :: Python :: 3.7", "Programming Language :: Python :: 3.8", ], diff --git a/tests/example_experiment/conduct.py b/tests/example_experiment/conduct.py index 0738f84..d0e2b53 100755 --- a/tests/example_experiment/conduct.py +++ b/tests/example_experiment/conduct.py @@ -1,4 +1,3 @@ -# -*- coding: future_fstrings -*- import os from pathlib import Path diff --git a/tests/test_artifact.py b/tests/test_artifact.py index 0638c9b..133773c 100644 --- a/tests/test_artifact.py +++ b/tests/test_artifact.py @@ -1,4 +1,3 @@ -# -*- coding: future_fstrings -*- import imghdr import os import pickle @@ -6,10 +5,11 @@ import IPython import pandas as pd import pytest -from incense import artifact from IPython.display import HTML from pytest import raises +from incense import artifact + def test_repr(loader): exp = loader.find_by_id(3) diff --git a/tests/test_experiment.py b/tests/test_experiment.py index 730266b..eb55a09 100644 --- a/tests/test_experiment.py +++ b/tests/test_experiment.py @@ -1,4 +1,3 @@ -# -*- coding: future_fstrings -*- import collections.abc from datetime import datetime from fractions import Fraction diff --git a/tests/test_experiment_loader.py b/tests/test_experiment_loader.py index 0f8163a..c7aedbf 100644 --- a/tests/test_experiment_loader.py +++ b/tests/test_experiment_loader.py @@ -1,12 +1,12 @@ -# -*- coding: future_fstrings -*- from pathlib import Path import pytest -from incense.experiment import Experiment, FileSystemExperiment -from incense.experiment_loader import FileSystemExperimentLoader from pytest import raises from sacred import Experiment as SacredExperiment +from incense.experiment import Experiment, FileSystemExperiment +from incense.experiment_loader import FileSystemExperimentLoader + def test_find_by_id(loader): exp = loader.find_by_id(1) diff --git a/tests/test_projector.py b/tests/test_projector.py index 9ff855b..44825a1 100644 --- a/tests/test_projector.py +++ b/tests/test_projector.py @@ -1,4 +1,3 @@ -# -*- coding: future_fstrings -*- import numpy as np import pandas as pd from pandas.testing import assert_frame_equal diff --git a/tests/test_set_save.py b/tests/test_set_save.py index 6e293da..48e3078 100644 --- a/tests/test_set_save.py +++ b/tests/test_set_save.py @@ -1,4 +1,3 @@ -# -*- coding: future_fstrings -*- import os diff --git a/tox.ini b/tox.ini index 95eb813..a948f91 100644 --- a/tox.ini +++ b/tox.ini @@ -1,9 +1,8 @@ [tox] -envlist = py35, py36, py37, py38, mypy +envlist = py37, py38, mypy [travis] python = - 3.6: py36 3.7: py37 3.8: py38, mypy @@ -20,4 +19,4 @@ basepython = python3.8 commands = mypy --package incense deps = - mypy==0.780 + mypy==0.960 diff --git a/versioneer.py b/versioneer.py index 72cf33d..22806f5 100644 --- a/versioneer.py +++ b/versioneer.py @@ -277,6 +277,7 @@ from __future__ import print_function +import configparser import errno import json import os @@ -284,11 +285,6 @@ import subprocess import sys -try: - import configparser -except ImportError: - import ConfigParser as configparser - class VersioneerConfig: """Container for Versioneer configuration parameters."""