def test_DTWKmeans_init_clusters_acceptance_range(self,num_clusters):
if num_clusters < 1:
with pytest.raises(ValueError):
clts = DTWKmeans(num_clust = num_clusters)
else:
clts = DTWKmeans(num_clust = num_clusters)
assert clts
def test_DTWKmeans_init_warp_acceptance_range(self,warp):
num_clusters = 5
if warp < 1:
with pytest.raises(ValueError):
clts = DTWKmeans(num_clust = num_clusters, w=warp)
else:
clts = DTWKmeans(num_clust = num_clusters, w = warp)
assert clts
def test_DTWKmeans_init_num_initializations_acceptance_range(self,initializations):
num_clusters = 5
if initializations < 1:
with pytest.raises(ValueError):
clts = DTWKmeans(num_clust = num_clusters, num_init= initializations)
else:
clts = DTWKmeans(num_clust = num_clusters, num_init= initializations )
assert clts
def test_DTWKmeans_single_num_init(self,num_init,expected_inertia):
list_of_series = flat_dataset(random_seed=101)
# running only 2 times even if list of random seeds is 10
random_seed = 22
clts = DTWKmeans(num_clust = 3, num_iter = 10, num_init = num_init,
w=1,euclidean=True,random_seed=random_seed)
clts.fit(list_of_series)
inertia=clts._inertia(list_of_series)
assert inertia == pytest.approx(expected_inertia,abs=1e-2)
def test_DTWKmeans_inertia_positive(self):
list_of_series = make_flat_dataset([-1.0,0,0.5],10,additive_noise_factor=0.3,level_noise_factor=0.3,lengths=[5])
num_clusters = 3
iterations = 1
random_seed = 101
clts_1 = DTWKmeans(num_clust = num_clusters, num_iter = iterations, random_seed=random_seed)
clts_1.fit(list_of_series)
intertia = clts_1._inertia(list_of_series)
assert intertia > 0
def test_centroids_fitting_data_simple_slopes(self):
"""Example of clustering with simple slopes series
After fit, centroids values shall match data values
"""
slopes = [0.3,0,-0.3]
list_of_series = make_slopes_dataset(slopes,10,additive_noise_factor=0.0,intercept_noise_factor=0.0,lengths=[3])
# setting random seed othewise fitting may end with two matching centroids
clts = DTWKmeans(num_clust = 3, num_iter = 10, w=1,euclidean=True,random_seed=101)
clts.fit(list_of_series)
df_data = pd.DataFrame(list_of_series).drop_duplicates().sort_values(by=2)
df_centroids = pd.DataFrame(clts.cluster_centers_).drop_duplicates().sort_values(by=2)
assert np.allclose(df_data,df_centroids)
def test_DTWKmeans_fit_is_reproduceable_using_random_seed(self):
list_of_series = make_flat_dataset([-1.0,0,1.0],10,additive_noise_factor=0.1,level_noise_factor=0.1,lengths=[5])
num_clusters = 3
iterations = 1
random_seed = 101
clts_1 = DTWKmeans(num_clust = num_clusters, num_iter = iterations, random_seed=random_seed)
clts_1.fit(list_of_series)
df1 = pd.DataFrame(clts_1.cluster_centers_)
clts_2 = DTWKmeans(num_clust = num_clusters, num_iter = iterations, random_seed=random_seed)
clts_2.fit(list_of_series)
df2 = pd.DataFrame(clts_2.cluster_centers_)
assert np.all(df1.values==df2.values)
def test_example(self):
"""Example of clustering usage as defined in the docstring of DTWKmeans class"""
num_clusters = 2
iterations = 5
ts1 = 2.5 * np.random.randn(100,) + 3
X_1 = pd.Series(ts1)
ts2 = 2 * np.random.randn(100,) + 5
X_2 = pd.Series(ts2)
ts3 = -2.5 * np.random.randn(100,) + 3
X_3 = pd.Series(ts3)
list_of_series = [X_1, X_2, X_3]
from pynuTS.clustering import DTWKmeans
clts = DTWKmeans(num_clust = num_clusters, num_iter = iterations)
clts.fit(list_of_series)
ts4 = 3.5 * np.random.randn(100,) + 2
ts5 = -3.5 * np.random.randn(100,) + 2
X_4 = pd.Series(ts4)
X_5 = pd.Series(ts5)
list_new = [X_4, X_5]
clustering_dict = clts.predict(list_new)
assert type(clustering_dict) is dict
assert len(clustering_dict) == num_clusters
def test_DTWKmeans_init_random_seed(self,seed):
num_clusters = 5
clts = DTWKmeans(num_clust = num_clusters, random_seed = seed)
assert clts
def test_DTWKmeans_init_default_kwargs(self):
num_clusters = 2
clts = DTWKmeans(num_clust = num_clusters)
assert clts
def test_DTWKmeans_inertia_decrease_with_iteration_increase(self):
list_of_series = make_flat_dataset([-1.0,0,0.5],10,additive_noise_factor=0.3,level_noise_factor=0.3,lengths=[5])
num_clusters = 3
random_seed = 101
clts_1 = DTWKmeans(num_clust = num_clusters, num_iter=1, random_seed=random_seed)
clts_1.fit(list_of_series)
clts_2 = DTWKmeans(num_clust = num_clusters, num_iter=2, random_seed=random_seed)
clts_2.fit(list_of_series)
print (clts_1._inertia(list_of_series))
print (clts_2._inertia(list_of_series))
#assert False
assert clts_1._inertia(list_of_series) >= clts_2._inertia(list_of_series)
def test_DTWKmeans_init_criterion(self,euclidean,criterion):
num_clusters = 5
clts = DTWKmeans(num_clust = num_clusters, euclidean=euclidean)
assert clts.criterion == criterion