def test_arrays() -> None:
x = np.array([1,4,9,16])
if no.mpi.rank() == 0:
comm.send(x, dest=1)
if no.mpi.rank() == 1:
y = comm.recv(source=0)
assert np.array_equal(x,y)
df = pd.read_csv("./test/df2.csv")
if no.mpi.rank() == 0:
comm.send(df, dest=1)
if no.mpi.rank() == 1:
dfrec = comm.recv(source=0)
assert dfrec.equals(df)
i = "rank %d" % no.mpi.rank()
root = 0
i = comm.bcast(i, root=root)
# all procs should now have root process value
assert i == "rank 0"
# a0 will be different for each proc
a0 = np.random.rand(2,2)
a1 = comm.bcast(a0, root)
# a1 will equal a0 on rank 0 only
if no.mpi.rank() == 0:
assert np.array_equal(a0, a1)
else:
assert not np.array_equal(a0, a1)
# base model for MC engine
model = no.Model(no.NoTimeline(), no.MonteCarlo.deterministic_identical_stream)
# # check identical streams (independent=False)
u = model.mc.ustream(1000)
v = comm.bcast(u, root=root)
# u == v on all processes
assert np.array_equal(u, v)
# base model for MC engine
model = no.Model(no.NoTimeline(), no.MonteCarlo.deterministic_independent_stream)
# # check identical streams (independent=False)
u = model.mc.ustream(1000)
v = comm.bcast(u, root=root)
# u != v on all non-root processes
if no.mpi.rank() != root:
assert not np.array_equal(u, v)
else:
assert np.array_equal(u, v)
def test() -> None:
df = pd.read_csv("./test/df.csv")
# base model for MC engine
model = no.Model(no.NoTimeline(),
no.MonteCarlo.deterministic_identical_stream)
cats = np.array(range(4))
# identity matrix means no transitions
trans = np.identity(len(cats))
no.df.transition(model, cats, trans, df, "DC2101EW_C_ETHPUK11")
assert len(df["DC2101EW_C_ETHPUK11"].unique()
) == 1 and df["DC2101EW_C_ETHPUK11"].unique()[0] == 2
# NOTE transition matrix interpreted as being COLUMN MAJOR due to pandas DataFrame storing data in column-major order
# force 2->3
trans[2, 2] = 0.0
trans[2, 3] = 1.0
no.df.transition(model, cats, trans, df, "DC2101EW_C_ETHPUK11")
no.log(df["DC2101EW_C_ETHPUK11"].unique())
assert len(df["DC2101EW_C_ETHPUK11"].unique()
) == 1 and df["DC2101EW_C_ETHPUK11"].unique()[0] == 3
# ~half of 3->0
trans[3, 0] = 0.5
trans[3, 3] = 0.5
no.df.transition(model, cats, trans, df, "DC2101EW_C_ETHPUK11")
assert np.array_equal(np.sort(df["DC2101EW_C_ETHPUK11"].unique()),
np.array([0, 3]))
def test_errors() -> None:
df = pd.read_csv("./test/df.csv")
# base model for MC engine
model = no.Model(no.NoTimeline(),
no.MonteCarlo.deterministic_identical_stream)
cats = np.array(range(4))
# identity matrix means no transitions
trans = np.identity(len(cats))
# invalid transition matrices
assert_throws(ValueError, no.df.transition, model, cats, np.ones((1, 2)),
df, "DC2101EW_C_ETHPUK11")
assert_throws(ValueError, no.df.transition, model, cats, np.ones((1, 1)),
df, "DC2101EW_C_ETHPUK11")
assert_throws(ValueError, no.df.transition, model, cats, trans + 0.1, df,
"DC2101EW_C_ETHPUK11")
# category data MUST be 64bit integer. This will almost certainly be the default on linux/OSX (LP64) but maybe not on windows (LLP64)
df["DC2101EW_C_ETHPUK11"] = df["DC2101EW_C_ETHPUK11"].astype(np.int32)
assert_throws(TypeError, no.df.transition, model, cats, trans, df,
"DC2101EW_C_ETHPUK11")
def test_base() -> None:
base = no.Model(no.NoTimeline(),
no.MonteCarlo.deterministic_identical_stream)
assert_throws(
RuntimeError, no.run, base
) # RuntimeError: Tried to call pure virtual function "Model::step"
plt.show()
# %%
counts = crime.get_crime_counts()
print(counts)
outcomes = crime.get_crime_outcomes()
print(outcomes)
cats = crime.get_category_breakdown()
print(cats)
subcats = cats.loc["violence and sexual offences"]
print(subcats)
print(subcats.proportion.sum())
d = subcats.index.values
p = subcats.proportion.values
m = no.Model(no.Timeline.null(), no.MonteCarlo.deterministic_identical_stream)
print("%g" % (subcats.proportion.sum() - 1.0))
s = m.mc().sample(100, subcats.proportion.values / subcats.proportion.sum())
print(subcats.iloc[s].index)
print(counts.index.levels[1].unique())
print(cats.index.levels[0].unique())
# %%
# import importlib
# importlib.reload(crime.Crime)
# %%
# t = np.ones((3,3)) / 3
# no.df.transition(m, c, t, df, "n")
# no.log(df.n.value_counts())
# for i in c:
# no.log(df.n.value_counts()[i] > n/3 - sqrt(n) and df.n.value_counts()[i] < n/3 + sqrt(n))
# t = np.array([
# [1.0, 1.0, 1.0],
# [0.0, 0.0, 0.0],
# [0.0, 0.0, 0.0],
# ])
# no.df.transition(m, c, t, df, "n")
# no.log(df.n.value_counts())
if __name__ == "__main__":
m = no.Model(no.NoTimeline(), no.MonteCarlo.deterministic_identical_stream)
rows, tc, colcpp = cpp_impl(m, get_data())
no.log("C++ %d: %f" % (rows, tc))
m.mc.reset()
rows, tp, colpy = python_impl(m, get_data())
no.log("py %d: %f" % (rows, tp))
#no.log(colcpp-colpy)
assert np.array_equal(colcpp, colpy)
no.log("speedup factor = %f" % (tp / tc))
# f(m)
def test_basic() -> None:
# test unique index generation
idx = no.df.unique_index(100)
assert np.array_equal(
idx, np.arange(no.mpi.rank(), 100 * no.mpi.size(), step=no.mpi.size()))
idx = no.df.unique_index(100)
assert np.array_equal(
idx,
np.arange(100 * no.mpi.size() + no.mpi.rank(),
200 * no.mpi.size(),
step=no.mpi.size()))
N = 100000
# base model for MC engine
model = no.Model(no.NoTimeline(),
no.MonteCarlo.deterministic_identical_stream)
c = [1, 2, 3]
df = pd.DataFrame({"category": [1] * N})
# no transitions, check no changes
t = np.identity(3)
no.df.transition(model, c, t, df, "category")
assert df.category.value_counts()[1] == N
# all 1 -> 2
t[0, 0] = 0.0
t[0, 1] = 1.0
no.df.transition(model, c, t, df, "category")
assert 1 not in df.category.value_counts()
assert df.category.value_counts()[2] == N
# 2 -> 1 or 3
t = np.array([
[1.0, 0.0, 0.0],
[0.5, 0.0, 0.5],
[0.0, 0.0, 1.0],
])
no.df.transition(model, c, t, df, "category")
assert 2 not in df.category.value_counts()
for i in [1, 3]:
assert df.category.value_counts()[i] > N / 2 - sqrt(
N) and df.category.value_counts()[i] < N / 2 + sqrt(N)
# spread evenly
t = np.ones((3, 3)) / 3
no.df.transition(model, c, t, df, "category")
for i in c:
assert df.category.value_counts()[i] > N / 3 - sqrt(
N) and df.category.value_counts()[i] < N / 3 + sqrt(N)
# all -> 1
t = np.array([
[1.0, 0.0, 0.0],
[1.0, 0.0, 0.0],
[1.0, 0.0, 0.0],
])
no.df.transition(model, c, t, df, "category")
assert df.category.value_counts()[1] == N
no.log(str(t))
no.log(t.__str__())
no.log(no.time.isnever(1.234))
no.log(no.time.isnever(no.time.distant_past()))
no.log(no.time.isnever(no.time.far_future()))
no.log(no.time.isnever(no.time.never()))
while not t.at_end():
no.log((t.index(), t.at_checkpoint(), t.at_end()))
t.next()
no.log((t.index(), t.at_checkpoint(), t.at_end()))
# for _ in t:
# no.log()
m = no.Model(t, no.MonteCarlo.deterministic_identical_seed)
m.modify(no.mpi.rank())
try:
m.step()
except NotImplementedError as e:
no.log(e)
else:
assert False, "expected error, didnt get one"
assert m.check()
try:
m.checkpoint()
except NotImplementedError as e: