def test_param_sim_summ():
lhd = LatinHypercube(dmin, dmax)
n_points = 10
lhd.generate_array(n_points)
summ = lambda x: generate_tsfresh_features(x, MinimalFCParameters())
graph_dict = core.get_graph_chunked(param_func=lhd.draw,
sim_func=simulator2,
summaries_func=summ,
batch_size=n_points,
chunk_size=2)
assert len(
graph_dict["parameters"]) == 5, "Core test failed, dimensions mismatch"
assert len(graph_dict["trajectories"]
) == 5, "Core test failed, dimensions mismatch"
assert len(
graph_dict["summarystats"]) == 5, "Core test failed, expected None"
params, sim, summaries = dask.compute(graph_dict["parameters"],
graph_dict["trajectories"],
graph_dict["summarystats"])
sim = np.asarray(sim)
params = np.asarray(params)
summaries = np.asarray(summaries)
assert params.shape == (5, 2, 5), "Core test failed, dimensions mismatch"
assert sim.shape == (5, 2, 1, 2,
101), "Core test failed, dimensions mismatch"
assert summaries.shape == (5, 2, 1,
16), "Core test failed, dimensions mismatch"
fixed_data = np.asarray([simulator2(bound) for p in range(10)])
print(fixed_data.shape)
fixed_data = fixed_data.reshape(10, 2, 101)
fixed_mean = core.get_fixed_mean(fixed_data, summ, chunk_size=2)
m, = dask.compute(fixed_mean)
m = np.asarray(m)
assert m.shape == (1, 16), "Core test failed, dimensions mismatch"
dist_class = ns.NaiveSquaredDistance()
dist_func = lambda x: dist_class.compute(x, m)
dist = core.get_distance(dist_func, graph_dict["summarystats"])
assert len(dist) == 5, "Core test failed, dimesnion mismatch"
dist_res, = dask.compute(dist)
dist_res = np.asarray(dist_res)
assert dist_res.shape == (5, 2, 1,
16), "Core test failed, dimension mismatch"
def compute_fixed_mean(self, chunk_size):
"""
Computes the mean over summary statistics on fixed data
Parameters
----------
chunk_size : int
the partition size when splitting the fixed data. For avoiding many individual tasks
in dask if the data is large
Returns
-------
ndarray
scaled distance
"""
stats_mean = core.get_fixed_mean(self.data, self.summaries_function, chunk_size)
self.fixed_mean = stats_mean.compute()
del stats_mean
def compute_fixed_mean(self, chunk_size):
stats_mean = core.get_fixed_mean(self.data, self.summaries_function,
chunk_size)
self.fixed_mean = stats_mean.compute()
del stats_mean