def test_recommend_programming_idioms(capsys):
path = Path("examples/idioms/programs_db.json")
rec = Recommendations(db=json.loads(path.read_text()))
rec.run_pipeline()
output_path = path.parent / "programs_recommendations.md"
rec.get_markdown() # for coverage
make_snapshot(
output_path,
rec.get_markdown(sorting_strategy="lexicographic", grouping_strategy="no_group"),
capsys,
)
def test_recommend_program(capsys):
rec = Recommendations(
db=json.loads(Path("examples/dummy/programs_db.json").read_text()),
base_path=Path("examples/dummy/"),
)
rec.run_pipeline(literal_eval(Path("examples/dummy/pipe.py").read_text()))
print(rec.selected_programs)
assert rec.selected_programs == {
"prg2.py",
# "O/N/P",
# "Y/T/Q",
# "Y",
# "X/S/M/L/R/D",
# "O",
# "O/C/H/B",
# "X/S/M",
# "X/S/M/L/R",
# "Y/T",
# "O/C",
# "X/G",
# "X/S/M/L/V",
# "O/C/H/B/I",
"prg3.py",
# "O/N/P",
# "X/K",
# "Y/T",
# "X/S/M/L/V",
# "O/C/H/B",
# "X/S/M/L/R",
# "O/J",
# "X/S/M",
# "O/C/F/U",
# "O/C/H",
# "X/S",
# "Y",
# "O",
# "X/S/M/L",
# "Y/E",
}
print(rec.result)
assert rec.result == [
(5, "impart", ["prg8.py"]),
(6, "exclude", ["prg7.py", "prg9.py"]),
(7, "exclude", ["prg4.py", "prg5.py", "prg6.py"]),
(8, "include", ["prg1.py"]),
(9, "hide", []),
]
costs = {
taxon: rec.assess.taxon_cost(taxon)
for taxon in rec.db_programs["prg2.py"]["taxa"]
}
print(costs)
assert costs == {
"O/N/P": 0,
"Y/T/Q": 0.375,
"Y": 0,
"X/S/M/L/R/D": 0,
"O": 0,
"O/C/H/B": 0,
"X/S/M": 0,
"X/S/M/L/R": 0,
"Y/T": 0.25,
"O/C": 0,
"X/G": 0.25,
"X/S/M/L/V": 0,
"O/C/H/B/I": 0.03125,
}
text = rec.get_markdown(span_column_width=10)
make_snapshot(Path("examples/dummy/programs_recommendations.md"), text,
capsys)
def test_recommend_program(capsys):
rec = Recommendations(
commands=literal_eval(Path("tests/data/dummy/pipe.py").read_text()),
db=json.loads(Path("tests/data/dummy/db.json").read_text()),
base_path=Path("tests/data/dummy/"),
)
rec.run_pipeline()
print(rec.selected_programs)
assert rec.selected_programs == {
"prg2.py": [
"O/N/P",
"Y/T/Q",
"Y",
"X/S/M/L/R/D",
"O",
"O/C/H/B",
"X/S/M",
"X/S/M/L/R",
"Y/T",
"O/C",
"X/G",
"X/S/M/L/V",
"O/C/H/B/I",
],
"prg3.py": [
"O/N/P",
"X/K",
"Y/T",
"X/S/M/L/V",
"O/C/H/B",
"X/S/M/L/R",
"O/J",
"X/S/M",
"O/C/F/U",
"O/C/H",
"X/S",
"Y",
"O",
"X/S/M/L",
"Y/E",
],
}
assert [p["filtered_out"] for p in rec.commands] == [
["prg8.py"],
["prg7.py", "prg9.py"],
["prg4.py", "prg5.py", "prg6.py"],
["prg1.py"],
]
costs = {
taxon: rec.taxon_cost(taxon)
for taxon in rec.selected_programs["prg2.py"]
}
print(costs)
assert costs == {
"O/N/P": 0,
"Y/T/Q": 0.375,
"Y": 0,
"X/S/M/L/R/D": 0,
"O": 0,
"O/C/H/B": 0,
"X/S/M": 0,
"X/S/M/L/R": 0,
"Y/T": 0.25,
"O/C": 0,
"X/G": 0.25,
"X/S/M/L/V": 0,
"O/C/H/B/I": 0.03125,
}
text = rec.get_markdown(span_column_width=10)
make_snapshot(Path("tests/data/dummy/recommendations.md"), text, capsys)