def test_dask_pipeline_multiple_outputs():
clear_cache(CACHE_DIR)
@delayed()
@cached(folder=CACHE_DIR)
def load_data():
# time.sleep(1)
return [1, 1, 1, 2, 2, 2]
@delayed(nout=2)
@cached(folder=CACHE_DIR, nout=2)
def split_data(data):
return data[:3], data[3:]
@delayed()
@cached(folder=CACHE_DIR)
def compute_sum(arr):
# time.sleep(1)
return sum(arr)
data = load_data()
x, y = split_data(data)
xsum = compute_sum(x)
ysum = compute_sum(y)
# start = dt.datetime.utcnow()
(x_, y_, xsum_, ysum_) = delayed_compute((x, y, xsum, ysum))
# delay = (dt.datetime.utcnow() - start).total_seconds()
# assert 0.95 < delay < 1.95
assert x_ == [1, 1, 1]
assert y_ == [2, 2, 2]
assert xsum_ == 3
assert ysum_ == 6
def test_cached_with_args_kwargs_partial_ignore():
@cached(folder=CACHE_DIR, ignore_kwargs=["ts"])
def load_data(eps, ts):
time.sleep(1.0)
assert eps > 0
assert ts > pd.Timestamp("2000-01-01")
return eps
clear_cache(CACHE_DIR)
start = dt.datetime.utcnow()
res1 = load_data(eps=0.1, ts=pd.Timestamp("2010-01-01")).load()
delay = (dt.datetime.utcnow() - start).total_seconds()
assert delay > 0.95
start = dt.datetime.utcnow()
res2 = load_data(eps=0.1, ts=pd.Timestamp("2012-01-01")).load()
delay = (dt.datetime.utcnow() - start).total_seconds()
assert delay < 0.95
assert res1 == res2
start = dt.datetime.utcnow()
res3 = load_data(eps=0.2, ts=pd.Timestamp("2012-01-01")).load()
delay = (dt.datetime.utcnow() - start).total_seconds()
assert delay > 0.95
assert res1 != res3
def test_dask_pipeline_with_parameters_2_context():
clear_cache(CACHE_DIR)
@delayed()
@cached(folder=CACHE_DIR)
def load_data_1(ts: dt.datetime):
assert ts > dt.datetime(2019, 1, 1)
return 5
@delayed()
@cached(folder=CACHE_DIR)
def load_data_2(fix: float):
return 3 + fix
@delayed()
@cached(folder=CACHE_DIR)
def add(x, y):
return x + y
ts = DelayedParameter("ts", value=dt.datetime(2020, 1, 1))
fix = DelayedParameter("fix", value=0.5)
d1 = load_data_1(ts())
d2 = load_data_2(fix())
r = add(d1, d2)
(output,) = delayed_compute((r,))
assert abs(output - 8.5) < EPS
with ts.context(dt.datetime(2020, 2, 1)), fix.context(1.5):
(output,) = delayed_compute((r,))
assert abs(output - 9.5) < EPS
(output,) = delayed_compute((r,))
assert abs(output - 8.5) < EPS
def test_dask_pipeline_sequential_runs():
clear_cache(CACHE_DIR)
@delayed()
@cached(folder=CACHE_DIR)
def load_data_1():
time.sleep(1)
return 5
@delayed()
@cached(folder=CACHE_DIR)
def load_data_2():
time.sleep(1)
return 3
@delayed()
@cached(folder=CACHE_DIR)
def add(x, y):
return x + y
d1 = load_data_1()
d2 = load_data_2()
r = add(d1, d2)
start = dt.datetime.utcnow()
d1_, d2_ = delayed_compute((d1, d2))
delay = (dt.datetime.utcnow() - start).total_seconds()
assert 0.95 < delay < 1.95
start = dt.datetime.utcnow()
(output,) = delayed_compute((r,))
delay = (dt.datetime.utcnow() - start).total_seconds()
assert delay < 0.95
assert output == 8
def test_cached_load_and_hash(data, ftype):
@cached(folder=CACHE_DIR, ftype=ftype, override=False)
def load_data():
return data
@cached(folder=CACHE_DIR, ftype="pickle", override=False)
def compute_data(data):
return 0
clear_cache(CACHE_DIR)
loaded = load_data().load()
_ = compute_data(loaded).load()
assert _count_cache_files() == 4
loaded = load_data().load()
_ = compute_data(loaded).load()
assert _count_cache_files() == 4
if isinstance(data, pd.Series):
pd.testing.assert_series_equal(loaded, data)
elif isinstance(data, pd.DataFrame):
pd.testing.assert_frame_equal(loaded, data)
elif isinstance(data, np.ndarray):
np.testing.assert_equal(loaded, data)
else:
assert loaded == data
def test_delayed_cached_another_cache_dir():
another_cache_dir = "cache/temp2/"
@delayed_cached(folder=another_cache_dir)
def load_data():
return 1
clear_cache(another_cache_dir)
r = load_data()
_ = delayed_compute((r,))
assert (Path(another_cache_dir) / "load_data.pickle").exists()
def test_dask_pipeline_with_parameters_2():
clear_cache(CACHE_DIR)
@delayed()
@cached(folder=CACHE_DIR)
def load_data_1(ts: dt.datetime):
assert ts > dt.datetime(2019, 1, 1)
time.sleep(1)
return 5
@delayed()
@cached(folder=CACHE_DIR)
def load_data_2(fix: float):
time.sleep(1)
return 3 + fix
@delayed()
@cached(folder=CACHE_DIR)
def add(x, y):
return x + y
ts = DelayedParameter("ts", value=dt.datetime(2020, 1, 1))
fix = DelayedParameter("fix", value=0.5)
d1 = load_data_1(ts())
d2 = load_data_2(fix())
r = add(d1, d2)
start = dt.datetime.utcnow()
(output,) = delayed_compute((r,))
delay = (dt.datetime.utcnow() - start).total_seconds()
assert 0.95 < delay < 1.95
assert abs(output - 8.5) < EPS
start = dt.datetime.utcnow()
(output,) = delayed_compute((r,))
delay = (dt.datetime.utcnow() - start).total_seconds()
assert delay < 0.95
assert abs(output - 8.5) < EPS
ts.set(dt.datetime(2020, 2, 1))
fix.set(1.5)
start = dt.datetime.utcnow()
(output,) = delayed_compute((r,))
delay = (dt.datetime.utcnow() - start).total_seconds()
assert 0.95 < delay < 1.95
assert abs(output - 9.5) < EPS
start = dt.datetime.utcnow()
(output,) = delayed_compute((r,))
delay = (dt.datetime.utcnow() - start).total_seconds()
assert delay < 0.95
assert abs(output - 9.5) < EPS
def test_cached_long_name_many_arguments():
@cached(folder=CACHE_DIR, override=False)
def compute(*args):
time.sleep(1)
return sum(len(line) for line in args)
clear_cache(CACHE_DIR)
start = dt.datetime.utcnow()
_ = compute(*("a" * 20 for _ in range(20))).load()
delay = (dt.datetime.utcnow() - start).total_seconds()
assert delay > 0.95
start = dt.datetime.utcnow()
_ = compute(*("a" * 20 for _ in range(20))).load()
delay = (dt.datetime.utcnow() - start).total_seconds()
assert delay < 0.95
def test_cached_long_name_one_long_argument():
@cached(folder=CACHE_DIR, override=False)
def compute(line):
time.sleep(1)
return len(line)
clear_cache(CACHE_DIR)
start = dt.datetime.utcnow()
_ = compute("a" * 300).load()
delay = (dt.datetime.utcnow() - start).total_seconds()
assert delay > 0.95
start = dt.datetime.utcnow()
_ = compute("a" * 300).load()
delay = (dt.datetime.utcnow() - start).total_seconds()
assert delay < 0.95
def test_cached_load_time():
@cached(folder=CACHE_DIR, override=False)
def load_data():
time.sleep(1)
return 1
clear_cache(CACHE_DIR)
start = dt.datetime.utcnow()
_ = load_data().load()
delay = (dt.datetime.utcnow() - start).total_seconds()
assert delay > 0.95
start = dt.datetime.utcnow()
_ = load_data().load()
delay = (dt.datetime.utcnow() - start).total_seconds()
assert delay < 0.95
def test_dask_cached_with_args_kwargs_load_compute(data, ftype, eps, ts):
@delayed()
@cached(folder=CACHE_DIR, ftype=ftype, override=False)
def load_data(eps, ts):
assert eps > 0
assert ts > pd.Timestamp("2000-01-01")
return data
clear_cache(CACHE_DIR)
r = load_data(eps, ts=ts)
_ = r.compute()
loaded = r.compute().load()
if isinstance(data, pd.Series):
pd.testing.assert_series_equal(loaded, data)
elif isinstance(data, pd.DataFrame):
pd.testing.assert_frame_equal(loaded, data)
elif isinstance(data, np.ndarray):
np.testing.assert_equal(loaded, data)
else:
assert loaded == data
def test_cached_with_args_kwargs_load(data, ftype, eps, ts):
@cached(folder=CACHE_DIR, ftype=ftype, override=False)
def load_data(eps, ts):
assert eps > 0
assert ts > pd.Timestamp("2000-01-01")
return data
clear_cache(CACHE_DIR)
_ = load_data(eps, ts=ts).load()
assert _count_cache_files() == 2
loaded = load_data(eps, ts=ts).load()
assert _count_cache_files() == 2
if isinstance(data, pd.Series):
pd.testing.assert_series_equal(loaded, data)
elif isinstance(data, pd.DataFrame):
pd.testing.assert_frame_equal(loaded, data)
elif isinstance(data, np.ndarray):
np.testing.assert_equal(loaded, data)
else:
assert loaded == data, str(loaded)
def test_dask_pipeline_with_parameters_private():
clear_cache(CACHE_DIR)
@delayed()
@cached(folder=CACHE_DIR)
def load_data_1(_ts: dt.datetime):
time.sleep(1)
assert _ts > dt.datetime(2019, 1, 1)
return 5
@delayed()
@cached(folder=CACHE_DIR)
def load_data_2(fix: float):
time.sleep(1)
return 3 + fix
@delayed()
@cached(folder=CACHE_DIR)
def add(x, y):
return x + y
params = DelayedParameters()
_ts = params.create("_ts", value=dt.datetime(2020, 1, 1))
fix = params.create("fix", value=0.5)
d1 = load_data_1(_ts=_ts)
d2 = load_data_2(fix=fix)
r = add(d1, d2)
start = dt.datetime.utcnow()
(output,) = delayed_compute((r,))
delay = (dt.datetime.utcnow() - start).total_seconds()
assert 0.95 < delay < 1.95
with params.context({"_ts": dt.datetime(2020, 2, 1)}):
start = dt.datetime.utcnow()
(output,) = delayed_compute((r,))
delay = (dt.datetime.utcnow() - start).total_seconds()
assert delay < 0.95
def test_dask_pipeline_with_parameters_create_many():
clear_cache(CACHE_DIR)
@delayed()
@cached(folder=CACHE_DIR)
def load_data_1(ts: dt.datetime):
assert ts > dt.datetime(2019, 1, 1)
time.sleep(1)
return 5
@delayed()
@cached(folder=CACHE_DIR)
def load_data_2(fix: float):
time.sleep(1)
return 3 + fix
@delayed()
@cached(folder=CACHE_DIR)
def add(x, y):
return x + y
params = DelayedParameters()
params.create_many(
{
"ts": dt.datetime(2020, 1, 1),
"fix": 0.5,
}
)
print(params.get_params())
d2 = load_data_2(params.get_delayed("fix"))
d1 = load_data_1(params.get_delayed("ts"))
r = add(d1, d2)
start = dt.datetime.utcnow()
(output,) = delayed_compute((r,))
delay = (dt.datetime.utcnow() - start).total_seconds()
assert 0.95 < delay < 1.95
assert abs(output - 8.5) < EPS
def test_cached_with_chained_df_load_and_hash(data, output, ftype):
@cached(folder=CACHE_DIR, ftype=ftype, override=False)
def load_data():
return data
@cached(folder=CACHE_DIR, ftype=ftype, override=False)
def process_data(df):
return df.dropna()
clear_cache(CACHE_DIR)
df = load_data()
_ = process_data(df).load()
df = load_data()
processed = process_data(df).load()
if isinstance(data, pd.Series):
pd.testing.assert_series_equal(processed, output)
elif isinstance(data, pd.DataFrame):
pd.testing.assert_frame_equal(processed, output)
elif isinstance(data, np.ndarray):
np.testing.assert_equal(processed, output)
else:
assert processed == output
