def test_hartigans_rule(self, model_national):
data = model_national._model_data
with pytest.warns(exceptions.ModelWarning) as excinfo:
funcs.apply_clustering(
data,
timesteps=None,
clustering_func="kmeans",
how="mean",
normalize=True,
)
assert check_error_or_warning(excinfo, "a good number of clusters is 5")
def test_hierarchical_no_hartigans_rule(self, model_national):
data = model_national._model_data
with pytest.raises(exceptions.ModelError) as excinfo:
funcs.apply_clustering(
data,
timesteps=None,
clustering_func="hierarchical",
how="mean",
normalize=True,
)
assert check_error_or_warning(
excinfo, "Cannot undertake hierarchical clustering")
def test_15min_clustering(self):
# The data is identical for '2005-01-01' and '2005-01-03' timesteps,
# it is only different for '2005-01-02'
override = {
"techs.test_demand_elec.constraints.resource":
"file=demand_elec_15mins.csv",
"model.subset_time": None,
}
model = build_test_model(override, scenario="simple_supply,one_day")
data = model._model_data
data_clustered_kmeans = funcs.apply_clustering(
data,
timesteps=None,
clustering_func="kmeans",
how="mean",
normalize=True,
k=2,
)
data_clustered_hierarchical = funcs.apply_clustering(
data,
timesteps=None,
clustering_func="hierarchical",
how="mean",
normalize=True,
k=2,
)
assert len(data_clustered_kmeans.clusters.to_pandas().unique()) == 2
assert len(
data_clustered_hierarchical.clusters.to_pandas().unique()) == 2
days = np.unique(
data_clustered_kmeans.timesteps.to_index().strftime("%Y-%m-%d"))
# not sure which of '2005-01-01' and '2005-01-03' it will choose to
# label the cluster of those two days
assert "2005-01-02" in days and ("2005-01-01" in days
or "2005-01-03" in days)
assert np.array_equal(
data_clustered_kmeans.timestep_resolution.values,
[0.25 for i in range(24 * 4 * 2)],
)
def test_hierarchical_closest(self, model_national):
data = model_national._model_data
data_clustered = funcs.apply_clustering(
data,
timesteps=None,
clustering_func="hierarchical",
how="closest",
normalize=True,
k=5,
)
def test_hierarchical_mean(self, model_national):
data = model_national._model_data
data_clustered = funcs.apply_clustering(
data,
timesteps=None,
clustering_func="hierarchical",
how="mean",
normalize=True,
k=5,
)
assert len(data_clustered.clusters.to_pandas().unique()) == 5
