共享博客more_itertools 无法在 Python 3.6 中从 functools 导入cached_property
问题内容
我尝试使用以下命令从 visual studio 代码中的终端运行grade_analysis.py：
~/documents/school/ml4t_2023fall/assess_portfolio$ pythonpath=../:. python grade_analysis.py根据班级设置说明
但是，当我运行命令时，grade_analysis.py 似乎无法提升级别并从 grading.grading.py 文件中获取信息。
我使用这个命令是错误的还是遗漏了什么？
这是我收到的错误：
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2023fall/assess_portfolio$ pythonpath=../:. python grade_analysis.pytraceback (most recent call last): file "grade_analysis.py", line 20, in <module> import pytest file "/home/clopez/miniconda3/envs/ml4t/lib/python3.6/site-packages/pytest.py", line 34, in <module> from _pytest.python_api import approx file "/home/clopez/miniconda3/envs/ml4t/lib/python3.6/site-packages/_pytest/python_api.py", line 13, in <module> from more_itertools.more import always_iterable file "/home/clopez/miniconda3/envs/ml4t/lib/python3.6/site-packages/more_itertools/__init__.py", line 3, in <module> from .more import * # noqa file "/home/clopez/miniconda3/envs/ml4t/lib/python3.6/site-packages/more_itertools/more.py", line 5, in <module> from functools import cached_property, partial, reduce, wrapsimporterror: cannot import name 'cached_property'</module></module></module></module></module>
环境设置说明
conda 环境 yml
name: ml4tchannels:- conda-forge- defaultsdependencies:- python=3.6- cycler=0.10.0- kiwisolver=1.1.0- matplotlib=3.0.3- numpy=1.16.3- pandas=0.24.2- pyparsing=2.4.0- python-dateutil=2.8.0- pytz=2019.1- scipy=1.2.1- seaborn=0.9.0- six=1.12.0- joblib=0.13.2- pytest=5.0- pytest-json=0.4.0- future=0.17.1- pprofile=2.0.2- pip- pip: - jsons==0.8.8 - gradescope-utils - subprocess32
等级分析.py
"""MC1-P1: Analyze a portfolio - grading script. Usage: - Switch to a student feedback directory first (will write "points.txt" and "comments.txt" in pwd). - Run this script with both ml4t/ and student solution in PYTHONPATH, e.g.: PYTHONPATH=ml4t:MC1-P1/jdoe7 python ml4t/mc1_p1_grading/grade_analysis.py Copyright 2017, Georgia Tech Research Corporation Atlanta, Georgia 30332-0415 All Rights Reserved """ import datetime import os import sys import traceback as tb from collections import OrderedDict, namedtuple import pandas as pd import pytest from grading.grading import ( GradeResult, IncorrectOutput, grader, run_with_timeout, ) from util import get_data # Student code # Spring '16 renamed package to just "analysis" (BPH) main_code = "analysis" # module name to import # Test cases # Spring '16 test cases only check sharp ratio, avg daily ret, and cum_ret (BPH) PortfolioTestCase = namedtuple( "PortfolioTestCase", ["inputs", "outputs", "description"] ) portfolio_test_cases = [ PortfolioTestCase( inputs=dict( start_date="2010-01-01", end_date="2010-12-31", symbol_allocs=OrderedDict( [("GOOG", 0.2), ("AAPL", 0.3), ("GLD", 0.4), ("XOM", 0.1)] ), start_val=1000000, ), outputs=dict( cum_ret=0.255646784534, avg_daily_ret=0.000957366234238, sharpe_ratio=1.51819243641, ), description="Wiki example 1", ), PortfolioTestCase( inputs=dict( start_date="2010-01-01", end_date="2010-12-31", symbol_allocs=OrderedDict( [("AXP", 0.0), ("HPQ", 0.0), ("IBM", 0.0), ("HNZ", 1.0)] ), start_val=1000000, ), outputs=dict( cum_ret=0.198105963655, avg_daily_ret=0.000763106152672, sharpe_ratio=1.30798398744, ), description="Wiki example 2", ), PortfolioTestCase( inputs=dict( start_date="2010-06-01", end_date="2010-12-31", symbol_allocs=OrderedDict( [("GOOG", 0.2), ("AAPL", 0.3), ("GLD", 0.4), ("XOM", 0.1)] ), start_val=1000000, ), outputs=dict( cum_ret=0.205113938792, avg_daily_ret=0.00129586924366, sharpe_ratio=2.21259766672, ), description="Wiki example 3: Six month range", ), PortfolioTestCase( inputs=dict( start_date="2010-01-01", end_date="2013-05-31", symbol_allocs=OrderedDict( [("AXP", 0.3), ("HPQ", 0.5), ("IBM", 0.1), ("GOOG", 0.1)] ), start_val=1000000, ), outputs=dict( cum_ret=-0.110888530433, avg_daily_ret=-6.50814806831e-05, sharpe_ratio=-0.0704694718385, ), description="Normalization check", ), PortfolioTestCase( inputs=dict( start_date="2010-01-01", end_date="2010-01-31", symbol_allocs=OrderedDict( [("AXP", 0.9), ("HPQ", 0.0), ("IBM", 0.1), ("GOOG", 0.0)] ), start_val=1000000, ), outputs=dict( cum_ret=-0.0758725033871, avg_daily_ret=-0.00411578300489, sharpe_ratio=-2.84503813366, ), description="One month range", ), PortfolioTestCase( inputs=dict( start_date="2011-01-01", end_date="2011-12-31", symbol_allocs=OrderedDict( [("WFR", 0.25), ("ANR", 0.25), ("MWW", 0.25), ("FSLR", 0.25)] ), start_val=1000000, ), outputs=dict( cum_ret=-0.686004563165, avg_daily_ret=-0.00405018240566, sharpe_ratio=-1.93664660013, ), description="Low Sharpe ratio", ), PortfolioTestCase( inputs=dict( start_date="2010-01-01", end_date="2010-12-31", symbol_allocs=OrderedDict( [("AXP", 0.0), ("HPQ", 1.0), ("IBM", 0.0), ("HNZ", 0.0)] ), start_val=1000000, ), outputs=dict( cum_ret=-0.191620333598, avg_daily_ret=-0.000718040989619, sharpe_ratio=-0.71237182415, ), description="All your eggs in one basket", ), PortfolioTestCase( inputs=dict( start_date="2006-01-03", end_date="2008-01-02", symbol_allocs=OrderedDict( [("MMM", 0.0), ("MO", 0.9), ("MSFT", 0.1), ("INTC", 0.0)] ), start_val=1000000, ), outputs=dict( cum_ret=0.43732715979, avg_daily_ret=0.00076948918955, sharpe_ratio=1.26449481371, ), description="Two year range", ), ] abs_margins = dict( cum_ret=0.001, avg_daily_ret=0.00001, sharpe_ratio=0.001 ) # absolute margin of error for each output points_per_output = dict( cum_ret=2.5, avg_daily_ret=2.5, sharpe_ratio=5.0 ) # points for each output, for partial credit points_per_test_case = sum(points_per_output.values()) max_seconds_per_call = 5 # Grading parameters (picked up by module-level grading fixtures) max_points = float(len(portfolio_test_cases) * points_per_test_case) html_pre_block = ( True # surround comments with HTML <pre class="brush:php;toolbar:false"> tag (for T-Square comments field) ) # Test functon(s) @pytest.mark.parametrize("inputs,outputs,description", portfolio_test_cases) def test_analysis(inputs, outputs, description, grader): """Test get_portfolio_value() and get_portfolio_stats() return correct values. Requires test inputs, expected outputs, description, and a grader fixture. """ points_earned = 0.0 # initialize points for this test case try: # Try to import student code (only once) if not main_code in globals(): import importlib # * Import module mod = importlib.import_module(main_code) globals()[main_code] = mod # Unpack test case start_date_str = inputs["start_date"].split("-") start_date = datetime.datetime( int(start_date_str[0]), int(start_date_str[1]), int(start_date_str[2]), ) end_date_str = inputs["end_date"].split("-") end_date = datetime.datetime( int(end_date_str[0]), int(end_date_str[1]), int(end_date_str[2]) ) symbols = list( inputs["symbol_allocs"].keys() ) # e.g.: ['GOOG', 'AAPL', 'GLD', 'XOM'] allocs = list( inputs["symbol_allocs"].values() ) # e.g.: [0.2, 0.3, 0.4, 0.1] start_val = inputs["start_val"] risk_free_rate = inputs.get("risk_free_rate", 0.0) # the wonky unpacking here is so that we only pull out the values we say we'll test. def timeoutwrapper_analysis(): student_rv = analysis.assess_portfolio( sd=start_date, ed=end_date, syms=symbols, allocs=allocs, sv=start_val, rfr=risk_free_rate, sf=252.0, gen_plot=False, ) return student_rv result = run_with_timeout( timeoutwrapper_analysis, max_seconds_per_call, (), {} ) student_cr = result[0] student_adr = result[1] student_sr = result[3] port_stats = OrderedDict( [ ("cum_ret", student_cr), ("avg_daily_ret", student_adr), ("sharpe_ratio", student_sr), ] ) # Verify against expected outputs and assign points incorrect = False msgs = [] for key, value in port_stats.items(): if abs(value - outputs[key]) > abs_margins[key]: incorrect = True msgs.append( " {}: {} (expected: {})".format( key, value, outputs[key] ) ) else: points_earned += points_per_output[key] # partial credit if incorrect: inputs_str = ( " start_date: {}" " end_date: {}" " symbols: {}" " allocs: {}" " start_val: {}".format( start_date, end_date, symbols, allocs, start_val ) ) raise IncorrectOutput( "One or more stats were incorrect. Inputs:{} Wrong" " values:{}".format(inputs_str, "".join(msgs)) ) except Exception as e: # Test result: failed msg = "Test case description: {}".format(description) # Generate a filtered stacktrace, only showing erroneous lines in student file(s) tb_list = tb.extract_tb(sys.exc_info()[2]) for i in range(len(tb_list)): row = tb_list[i] tb_list[i] = ( os.path.basename(row[0]), row[1], row[2], row[3], ) # show only filename instead of long absolute path tb_list = [row for row in tb_list if row[0] == "analysis.py"] if tb_list: msg += "Traceback:" msg += "".join(tb.format_list(tb_list)) # contains newlines msg += "{}: {}".format(e.__class__.__name__, str(e)) # Report failure result to grader, with stacktrace grader.add_result( GradeResult(outcome="failed", points=points_earned, msg=msg) ) raise else: # Test result: passed (no exceptions) grader.add_result( GradeResult(outcome="passed", points=points_earned, msg=None) ) if __name__ == "__main__": pytest.main(["-s", __file__])我已激活 conda 环境并设置文件，以便它应该能够访问 util.py 文件和 grading.py 文件。
我希望运行命令后，analysis.py 文件将使用grade_analysis.py 进行评分。
正确答案
这就是为什么使用 conda-lock 锁文件（或容器化）比使用 yaml 更能实现长期可重复性。附加依赖项（如 more-itertools）在 yaml 中不受限制，并且其他包的依赖项可能没有适当的上限。在这种情况下，op 最终得到了 more_itertools 模块的一个版本，该模块引用了后来才添加到 functools 的内容。
二分法显示了从 more_itertools v10 开始的有问题的引用（对 cached_property），因此设置上限应该可以解决问题：
name: ml4tchannels: - conda-forge - defaultsdependencies: - python=3.6 - cycler=0.10.0 - kiwisolver=1.1.0 - matplotlib=3.0.3 - more-itertools<p>使用此 yaml，并测试导致错误的导入现在可以正常工作：</p><pre class="brush:bash;toolbar:false;">$ python -c "from more_itertools.more import always_iterable"$ echo $?0
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