def single_chunk_blobs():
"""X, y pair for clustering
The `X` and `y` have a single block, so chunksize is 100.
Useful for testing `partial_fit` methods.
"""
X, y = make_blobs(chunks=100, random_state=0)
return X, y
def Xl_blobs_easy():
"""
Tuple of (X, labels) for classification.
The centers are very spread out, so the clustering is easy.
"""
centers = np.array([[-7, -7], [0, 0], [7, 7]])
X, y = make_blobs(cluster_std=0.1,
centers=centers,
chunks=50,
random_state=0)
return X, y
def _prep_data(self, reg=False):
self.n_samples = int(1e5)
self.chunk_size = int(1e4)
self.n_chunks = np.ceil(self.n_samples / self.chunk_size).astype(int)
if reg:
self.x, self.y = make_regression(n_samples=self.n_samples,
chunks=self.chunk_size,
random_state=0,
n_features=40)
else:
self.x, self.y = make_blobs(n_samples=self.n_samples,
chunks=self.chunk_size,
random_state=0,
n_features=40,
centers=2,
cluster_std=100)
return self
from functools import partial
import numpy as np
import pytest
import sklearn.cluster
from dask_ml import metrics
from dask_ml.cluster import SpectralClustering
from dask_ml.datasets import make_blobs
X, y = make_blobs(n_samples=200, chunks=100, random_state=0)
@pytest.mark.parametrize("as_ndarray", [False, True])
@pytest.mark.parametrize("persist_embedding", [True, False])
def test_basic(as_ndarray, persist_embedding):
sc = SpectralClustering(
n_components=25, random_state=0, persist_embedding=persist_embedding
)
if as_ndarray:
X_ = X.compute()
else:
X_ = X
sc.fit(X_)
assert len(sc.labels_) == len(X_)
@pytest.mark.parametrize(
"assign_labels", [sklearn.cluster.KMeans(n_init=2), "sklearn-kmeans"]
)
def test_sklearn_kmeans(assign_labels):
