J'ai essayé d'exécuter grade_analysis.py à partir du terminal dans le code Visual Studio à l'aide de la commande suivante :
Cependant, lorsque j'exécute la commande, grade_analysis.py ne semble pas pouvoir passer au niveau supérieur et obtenir les informations du fichier grading.grading.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: {}\n"
" end_date: {}\n"
" symbols: {}\n"
" allocs: {}\n"
" start_val: {}".format(
start_date, end_date, symbols, allocs, start_val
)
)
raise IncorrectOutput(
"One or more stats were incorrect.\n Inputs:\n{}\n Wrong"
" values:\n{}".format(inputs_str, "\n".join(msgs))
)
except Exception as e:
# Test result: failed
msg = "Test case description: {}\n".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:\n"
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__])Copier après la connexion
J'ai activé l'environnement conda et configuré les fichiers pour qu'il puisse accéder au fichier util.py et au fichier grading.py.
J'espère qu'après avoir exécuté la commande, le fichier Analysis.py sera noté à l'aide de grade_analysis.py.
C'est pourquoi il est préférable d'utiliser conda-lock pour verrouiller des fichiers (ou conteneuriser) pour une reproductibilité à long terme que d'utiliser yaml. Dépendances supplémentaires (telles que le contenu de 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.
Utilisez ce yaml et testez que l'importation à l'origine de l'erreur fonctionne désormais :
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