def test_targetencoder_multi_column():
"""
Test jointly encoding multiple columns
"""
train = cudf.DataFrame({
'cat_1': ['a', 'b', 'b', 'a', 'a', 'b'],
'cat_2': [1, 1, 2, 2, 1, 2],
'label': [1, 0, 1, 1, 0, 1]
})
test = cudf.DataFrame({
'cat_1': ['b', 'b', 'a', 'b'],
'cat_2': [1, 2, 1, 2]
})
encoder = TargetEncoder()
train_encoded = encoder.fit_transform(train[['cat_1', 'cat_2']],
train.label)
test_encoded = encoder.transform(test[['cat_1', 'cat_2']])
train_answer = np.array([2. / 3, 2. / 3, 1., 2. / 3, 2. / 3, 1.])
test_answer = np.array([0., 1., 0.5, 1.])
assert array_equal(train_encoded, train_answer)
assert array_equal(test_encoded, test_answer)
encoder = TargetEncoder()
encoder.fit(train[['cat_1', 'cat_2']], train.label)
train_encoded = encoder.transform(train[['cat_1', 'cat_2']])
test_encoded = encoder.transform(test[['cat_1', 'cat_2']])
assert array_equal(train_encoded, train_answer)
assert array_equal(test_encoded, test_answer)
def test_predict_proba(nrows, ncols, n_neighbors, n_clusters, datatype):
X, y = make_blobs(n_samples=nrows,
centers=n_clusters,
n_features=ncols,
cluster_std=0.01,
random_state=0)
X = X.astype(np.float32)
X_train, X_test, y_train, y_test = _build_train_test_data(X, y, datatype)
knn_cu = cuKNN(n_neighbors=n_neighbors)
knn_cu.fit(X_train, y_train)
predictions = knn_cu.predict_proba(X_test)
if datatype == "dataframe":
assert isinstance(predictions, cudf.DataFrame)
predictions = predictions.to_numpy()
y_test = y_test.to_numpy().reshape(y_test.shape[0])
else:
assert isinstance(predictions, np.ndarray)
y_hat = np.argmax(predictions, axis=1)
assert array_equal(y_hat.astype(np.int32), y_test.astype(np.int32))
assert array_equal(predictions.sum(axis=1), np.ones(y_test.shape[0]))
def test_predict_proba_multioutput(input_type, output_type):
X = np.array([[0, 0, 1, 0], [1, 0, 1, 0]]).astype(np.float32)
y = np.array([[15, 2], [5, 4]]).astype(np.int32)
if input_type == "cudf":
X = cudf.DataFrame(X)
y = cudf.DataFrame(y)
elif input_type == "cupy":
X = cp.asarray(X)
y = cp.asarray(y)
expected = (np.array([[0., 1.], [1., 0.]]).astype(np.float32),
np.array([[1., 0.], [0., 1.]]).astype(np.float32))
knn_cu = cuKNN(n_neighbors=1, output_type=output_type)
knn_cu.fit(X, y)
p = knn_cu.predict_proba(X)
assert isinstance(p, tuple)
for i in p:
if output_type == "cudf":
assert isinstance(i, cudf.DataFrame)
elif output_type == "numpy":
assert isinstance(i, np.ndarray)
elif output_type == "cupy":
assert isinstance(i, cp.ndarray)
assert array_equal(p[0].astype(np.float32), expected[0])
assert array_equal(p[1].astype(np.float32), expected[1])
def test_neighborhood_predictions(nrows, ncols, n_neighbors, n_clusters,
datatype):
X, y = make_blobs(n_samples=nrows,
centers=n_clusters,
n_features=ncols,
cluster_std=0.01,
random_state=0)
X = X.astype(np.float32)
X_train, X_test, y_train, y_test = _build_train_test_data(X, y, datatype)
knn_cu = cuKNN(n_neighbors=n_neighbors)
knn_cu.fit(X_train, y_train)
predictions = knn_cu.predict(X_test)
if datatype == "dataframe":
assert isinstance(predictions, cudf.Series)
assert array_equal(predictions.to_frame().astype(np.int32),
y_test.astype(np.int32))
else:
assert isinstance(predictions, np.ndarray)
assert array_equal(predictions.astype(np.int32),
y_test.astype(np.int32))
def test_basic_functions(labels, multipart, client):
fit_labels, xform_labels = labels
s = cp.asarray(fit_labels, dtype=np.int32)
df = dask.array.from_array(s)
s2 = cp.asarray(xform_labels, dtype=np.int32)
df2 = dask.array.from_array(s2)
if multipart:
df = df.rechunk((1, ))
df2 = df2.rechunk((1, ))
binarizer = LabelBinarizer(client=client, sparse_output=False)
binarizer.fit(df)
assert array_equal(cp.asnumpy(binarizer.classes_),
np.unique(cp.asnumpy(s)))
xformed = binarizer.transform(df2)
xformed = xformed.map_blocks(lambda x: x.get(), dtype=cp.float32)
xformed.compute_chunk_sizes()
assert xformed.compute().shape[1] == binarizer.classes_.shape[0]
original = binarizer.inverse_transform(xformed)
test = original.compute()
assert array_equal(cp.asnumpy(test), xform_labels)
def test_svd_flip():
x = cp.array(range(-10, 80)).reshape((9, 10))
u, s, v = cp.linalg.svd(x, full_matrices=False)
u_true, v_true = _svd_flip(u, v, u_based_decision=True)
reco_true = cp.dot(u_true * s, v_true)
u_false, v_false = _svd_flip(u, v, u_based_decision=False)
reco_false = cp.dot(u_false * s, v_false)
assert array_equal(reco_true, x)
assert array_equal(reco_false, x)
def test_pca_fit(data_info, input_type, client):
nrows, ncols, n_parts = data_info
if nrows == int(9e6) and pytest.max_gpu_memory < 48:
if pytest.adapt_stress_test:
nrows = nrows * pytest.max_gpu_memory // 256
ncols = ncols * pytest.max_gpu_memory // 256
else:
pytest.skip("Insufficient GPU memory for this test."
"Re-run with 'CUML_ADAPT_STRESS_TESTS=True'")
from cuml.dask.decomposition import TruncatedSVD as daskTPCA
from sklearn.decomposition import TruncatedSVD
from cuml.dask.datasets import make_blobs
X, _ = make_blobs(n_samples=nrows,
n_features=ncols,
centers=1,
n_parts=n_parts,
cluster_std=0.5,
random_state=10,
dtype=np.float32)
if input_type == "dataframe":
X_train = to_dask_cudf(X)
X_cpu = X_train.compute().to_pandas().values
elif input_type == "array":
X_train = X
X_cpu = cp.asnumpy(X_train.compute())
cutsvd = daskTPCA(n_components=5)
cutsvd.fit(X_train)
sktsvd = TruncatedSVD(n_components=5, algorithm="arpack")
sktsvd.fit(X_cpu)
all_attr = [
'singular_values_', 'components_', 'explained_variance_',
'explained_variance_ratio_'
]
for attr in all_attr:
with_sign = False if attr in ['components_'] else True
cuml_res = (getattr(cutsvd, attr))
if type(cuml_res) == np.ndarray:
cuml_res = cuml_res.to_numpy()
skl_res = getattr(sktsvd, attr)
if attr == 'singular_values_':
assert array_equal(cuml_res, skl_res, 1, with_sign=with_sign)
else:
assert array_equal(cuml_res, skl_res, 1e-1, with_sign=with_sign)
def test_compare_skl(nrows, ncols, nclusters, n_parts, n_neighbors,
streams_per_handle, reverse_worker_order, client):
from cuml.dask.neighbors import NearestNeighbors as daskNN
from sklearn.datasets import make_blobs
nrows = _scale_rows(client, nrows)
X, y = make_blobs(n_samples=int(nrows),
n_features=ncols,
centers=nclusters,
random_state=0)
X = X.astype(np.float32)
X_cudf = _prep_training_data(client, X, n_parts, reverse_worker_order)
from dask.distributed import wait
wait(X_cudf)
dist = np.array([len(v) for v in client.has_what().values()])
assert np.all(dist == dist[0])
cumlModel = daskNN(n_neighbors=n_neighbors,
streams_per_handle=streams_per_handle)
cumlModel.fit(X_cudf)
out_d, out_i = cumlModel.kneighbors(X_cudf)
local_i = np.array(out_i.compute().to_numpy(), dtype="int64")
sklModel = KNeighborsClassifier(n_neighbors=n_neighbors).fit(X, y)
skl_y_hat = sklModel.predict(X)
y_hat, _ = predict(local_i, y, n_neighbors)
sk_d, sk_i = sklModel.kneighbors(X)
sk_i = sk_i.astype("int64")
assert array_equal(local_i[:, 0], np.arange(nrows))
diff = sk_i - local_i
n_diff = len(diff[diff > 0])
perc_diff = n_diff / (nrows * n_neighbors)
assert perc_diff <= 3e-3
assert array_equal(y_hat, skl_y_hat)
def test_targetencoder_fit_transform():
train = cudf.DataFrame({
'category': ['a', 'b', 'b', 'a'],
'label': [1, 0, 1, 1]
})
encoder = TargetEncoder()
train_encoded = encoder.fit_transform(train.category, train.label)
answer = np.array([1., 1., 0., 1.])
assert array_equal(train_encoded, answer)
encoder = TargetEncoder()
encoder.fit(train.category, train.label)
train_encoded = encoder.transform(train.category)
assert array_equal(train_encoded, answer)
def test_one_category():
train = cudf.DataFrame({
'category': ['a', 'a', 'a', 'a'],
'label': [3, 0, 0, 3]
})
test = cudf.DataFrame({'category': ['c', 'b', 'a', 'd']})
encoder = TargetEncoder()
train_encoded = encoder.fit_transform(train.category, train.label)
answer = np.array([1., 2., 2., 1.])
assert array_equal(train_encoded, answer)
test_encoded = encoder.transform(test.category)
answer = np.array([1.5, 1.5, 1.5, 1.5])
assert array_equal(test_encoded, answer)
def test_targetencoder_var():
train = cudf.DataFrame({
'category': ['a', 'b', 'b', 'b'],
'label': [1, 0, 1, 1]
})
encoder = TargetEncoder(stat='var')
train_encoded = encoder.fit_transform(train.category, train.label)
answer = np.array([.25, 0., .5, .5])
assert array_equal(train_encoded, answer)
encoder = TargetEncoder(stat='var')
encoder.fit(train.category, train.label)
train_encoded = encoder.transform(train.category)
assert array_equal(train_encoded, answer)
def test_logistic_regression_weighting(regression_dataset,
option, test_status):
regression_type, data, coef, output = regression_dataset[test_status]
class_weight = None
sample_weight = None
if option == 'sample_weight':
n_samples = data.shape[0]
sample_weight = np.abs(np.random.rand(n_samples))
elif option == 'class_weight':
class_weight = np.random.rand(2)
class_weight = {0: class_weight[0], 1: class_weight[1]}
elif option == 'balanced':
class_weight = 'balanced'
culog = cuLog(fit_intercept=False, class_weight=class_weight)
culog.fit(data, output, sample_weight=sample_weight)
sklog = skLog(fit_intercept=False, class_weight=class_weight)
sklog.fit(data, output, sample_weight=sample_weight)
skcoef = np.squeeze(sklog.coef_)
cucoef = np.squeeze(culog.coef_)
if regression_type == 'binary':
skcoef /= np.linalg.norm(skcoef)
cucoef /= np.linalg.norm(cucoef)
unit_tol = 0.04
total_tol = 0.08
elif regression_type.startswith('multiclass'):
skcoef = skcoef.T
skcoef /= np.linalg.norm(skcoef, axis=1)[:, None]
cucoef /= np.linalg.norm(cucoef, axis=1)[:, None]
unit_tol = 0.2
total_tol = 0.3
equality = array_equal(skcoef, cucoef, unit_tol=unit_tol,
total_tol=total_tol)
if not equality:
print('\ncoef.shape: ', coef.shape)
print('coef:\n', coef)
print('cucoef.shape: ', cucoef.shape)
print('cucoef:\n', cucoef)
assert equality
cuOut = culog.predict(data)
skOut = sklog.predict(data)
assert array_equal(skOut, cuOut, unit_tol=unit_tol,
total_tol=total_tol)
def test_dbscan_default(name, client):
from cuml.dask.cluster.dbscan import DBSCAN as cuDBSCAN
eps = 0.5
default_base = {
'quantile': .3,
'eps': eps,
'damping': .9,
'preference': -200,
'n_neighbors': 10,
'n_clusters': 2
}
n_samples = 500
pat = get_pattern(name, n_samples)
params = default_base.copy()
params.update(pat[1])
X, y = pat[0]
X = StandardScaler().fit_transform(X)
cuml_dbscan = cuDBSCAN(output_type='numpy')
cu_labels = cuml_dbscan.fit_predict(X)
sk_dbscan = skDBSCAN(eps=params['eps'], min_samples=5)
sk_labels = sk_dbscan.fit_predict(X)
# Check the core points are equal
assert array_equal(cuml_dbscan.core_sample_indices_,
sk_dbscan.core_sample_indices_)
# Check the labels are correct
assert_dbscan_equal(sk_labels, cu_labels, X,
cuml_dbscan.core_sample_indices_, eps)
def test_predict_multioutput(input_type, output_type):
X = np.array([[0, 0, 1, 0], [1, 0, 1, 0]]).astype(np.float32)
y = np.array([[15, 2], [5, 4]]).astype(np.int32)
if input_type == "cudf":
X = cudf.DataFrame(X)
y = cudf.DataFrame(y)
elif input_type == "cupy":
X = cp.asarray(X)
y = cp.asarray(y)
knn_cu = cuKNN(n_neighbors=1, output_type=output_type)
knn_cu.fit(X, y)
p = knn_cu.predict(X)
if output_type == "cudf":
assert isinstance(p, cudf.DataFrame)
elif output_type == "numpy":
assert isinstance(p, np.ndarray)
elif output_type == "cupy":
assert isinstance(p, cp.ndarray)
assert array_equal(p.astype(np.int32), y)
def test_nonmonotonic_labels(n_classes, n_rows, n_cols, datatype, n_neighbors):
X, y = make_blobs(n_samples=n_rows,
centers=n_classes,
n_features=n_cols,
cluster_std=0.01,
random_state=0)
X = X.astype(np.float32)
# Draw labels from non-monotonically increasing set
classes = np.arange(0, n_classes * 5, 5)
for i in range(n_classes):
y[y == i] = classes[i]
X_train, X_test, y_train, y_test = _build_train_test_data(X, y, datatype)
knn_cu = cuKNN(n_neighbors=n_neighbors)
knn_cu.fit(X_train, y_train)
p = knn_cu.predict(X_test)
if datatype == "dataframe":
assert isinstance(p, cudf.Series)
p = p.to_frame().to_numpy().reshape(p.shape[0])
y_test = y_test.to_numpy().reshape(y_test.shape[0])
assert array_equal(p.astype(np.int32), y_test.astype(np.int32))
def test_umap_fit_transform_score(nrows, n_feats):
n_samples = nrows
n_features = n_feats
data, labels = make_blobs(n_samples=n_samples, n_features=n_features,
centers=10, random_state=42)
model = umap.UMAP(n_neighbors=10, min_dist=0.1)
cuml_model = cuUMAP(n_neighbors=10, min_dist=0.01)
embedding = model.fit_transform(data)
cuml_embedding = cuml_model.fit_transform(data, convert_dtype=True)
assert not np.isnan(embedding).any()
assert not np.isnan(cuml_embedding).any()
if nrows < 500000:
cuml_score = adjusted_rand_score(labels,
KMeans(10).fit_predict(
cuml_embedding))
score = adjusted_rand_score(labels,
KMeans(10).fit_predict(embedding))
assert array_equal(score, cuml_score, 1e-2, with_sign=True)
def test_batch_size(nrows, ncols, n_parts, batch_size, client):
n_neighbors = 10
n_clusters = 5
from cuml.dask.neighbors import NearestNeighbors as daskNN
from sklearn.datasets import make_blobs
nrows = _scale_rows(client, nrows)
X, y = make_blobs(n_samples=int(nrows),
n_features=ncols,
centers=n_clusters,
random_state=0)
X = X.astype(np.float32)
X_cudf = _prep_training_data(client, X, n_parts)
cumlModel = daskNN(n_neighbors=n_neighbors,
batch_size=batch_size,
streams_per_handle=5)
cumlModel.fit(X_cudf)
out_d, out_i = cumlModel.kneighbors(X_cudf)
local_i = out_i.compute().to_numpy()
y_hat, _ = predict(local_i, y, n_neighbors)
assert array_equal(y_hat, y)
def test_core_point_prop3():
params = {'eps': 1.1, 'min_samples': 4}
# The input looks like a two-barred (orhodox) cross or
# two stars sharing a link:
# . .
# . . . . .
# . .
# There are 2 core-points but they are not reachable from each other
# So there should be two clusters.
# However, the link that is shared between the stars
# actually has an ambiguous label (to the best of my knowledge)
# as it will depend on the order in which we process the core-points.
# So we exclude that point from the comparison with sklearn
# TODO: the above text does not correspond to the actual test!
X = np.array([[0, 0], [1, 0], [1, 1], [1, -1], [3, 0], [4, 0], [4, 1],
[4, -1], [5, 0], [2, 0]],
dtype=np.float32)
cuml_dbscan = cuDBSCAN(**params)
cu_labels = cuml_dbscan.fit_predict(X)
sk_dbscan = skDBSCAN(**params)
sk_labels = sk_dbscan.fit_predict(X)
# Check the core points are equal
assert array_equal(cuml_dbscan.core_sample_indices_,
sk_dbscan.core_sample_indices_)
# Check the labels are correct
assert_dbscan_equal(sk_labels, cu_labels, X,
cuml_dbscan.core_sample_indices_, params['eps'])
def test_pca_defaults(n_samples, n_features, sparse):
# FIXME: Disable the case True-300-200 due to flaky test
if sparse and n_features == 300 and n_samples == 200:
pytest.xfail('Skipping the case True-300-200 due to flaky test')
if sparse:
X = cupyx.scipy.sparse.random(n_samples,
n_features,
density=0.03,
dtype=cp.float32,
random_state=10)
else:
X, Y = make_multilabel_classification(n_samples=n_samples,
n_features=n_features,
n_classes=2,
n_labels=1,
random_state=1)
cupca = cuPCA()
cupca.fit(X)
curesult = cupca.transform(X)
cupca.handle.sync()
if sparse:
X = X.toarray().get()
skpca = skPCA()
skpca.fit(X)
skresult = skpca.transform(X)
assert skpca.svd_solver == cupca.svd_solver
assert cupca.components_.shape[0] == skpca.components_.shape[0]
assert curesult.shape == skresult.shape
assert array_equal(curesult, skresult, 1e-3, with_sign=False)
def test_ridge_regression_model(datatype, algorithm, nrows, column_info):
if algorithm == "svd" and nrows > 46340:
pytest.skip("svd solver is not supported for the data that has more"
"than 46340 rows or columns if you are using CUDA version"
"10.x")
ncols, n_info = column_info
X_train, X_test, y_train, y_test = make_regression_dataset(
datatype, nrows, ncols, n_info
)
# Initialization of cuML's ridge regression model
curidge = cuRidge(fit_intercept=False, normalize=False, solver=algorithm)
# fit and predict cuml ridge regression model
curidge.fit(X_train, y_train)
curidge_predict = curidge.predict(X_test)
if nrows < 500000:
# sklearn ridge regression model initialization, fit and predict
skridge = skRidge(fit_intercept=False, normalize=False)
skridge.fit(X_train, y_train)
skridge_predict = skridge.predict(X_test)
assert array_equal(skridge_predict,
curidge_predict,
1e-1,
with_sign=True)
def test_partial_fit(nrows, ncols, n_components, density, batch_size_divider,
whiten):
X, _ = make_blobs(n_samples=nrows, n_features=ncols, random_state=10)
cu_ipca = cuIPCA(n_components=n_components, whiten=whiten)
sample_size = int(nrows / batch_size_divider)
for i in range(0, nrows, sample_size):
cu_ipca.partial_fit(X[i:i + sample_size].copy())
cu_t = cu_ipca.transform(X)
cu_inv = cu_ipca.inverse_transform(cu_t)
sk_ipca = skIPCA(n_components=n_components, whiten=whiten)
X = cp.asnumpy(X)
for i in range(0, nrows, sample_size):
sk_ipca.partial_fit(X[i:i + sample_size].copy())
sk_t = sk_ipca.transform(X)
sk_inv = sk_ipca.inverse_transform(sk_t)
assert array_equal(cu_inv, sk_inv, 5e-5, with_sign=True)
def test_fit(nrows, ncols, n_components, sparse_input, density, sparse_format,
batch_size_divider, whiten):
if sparse_format == 'csc':
pytest.skip("cupyx.scipy.sparse.csc.csc_matrix does not support"
" indexing as of cupy 7.6.0")
if sparse_input:
X = cupyx.scipy.sparse.random(nrows,
ncols,
density=density,
random_state=10,
format=sparse_format)
else:
X, _ = make_blobs(n_samples=nrows, n_features=ncols, random_state=10)
cu_ipca = cuIPCA(n_components=n_components,
whiten=whiten,
batch_size=int(nrows / batch_size_divider))
cu_ipca.fit(X)
cu_t = cu_ipca.transform(X)
cu_inv = cu_ipca.inverse_transform(cu_t)
sk_ipca = skIPCA(n_components=n_components,
whiten=whiten,
batch_size=int(nrows / batch_size_divider))
if sparse_input:
X = X.get()
else:
X = cp.asnumpy(X)
sk_ipca.fit(X)
sk_t = sk_ipca.transform(X)
sk_inv = sk_ipca.inverse_transform(sk_t)
assert array_equal(cu_inv, sk_inv, 5e-5, with_sign=True)
def test_dbscan_propagation(datatype, use_handle, out_dtype, n_samples):
X, y = make_blobs(n_samples,
centers=1,
cluster_std=8.0,
center_box=(-100.0, 100.0),
random_state=8)
X = X.astype(datatype)
handle, stream = get_handle(use_handle)
eps = 0.5
cuml_dbscan = cuDBSCAN(handle=handle,
eps=eps,
min_samples=5,
output_type='numpy')
cu_labels = cuml_dbscan.fit_predict(X, out_dtype=out_dtype)
sk_dbscan = skDBSCAN(eps=eps, min_samples=5)
sk_labels = sk_dbscan.fit_predict(X)
# Check the core points are equal
assert array_equal(cuml_dbscan.core_sample_indices_,
sk_dbscan.core_sample_indices_)
# Check the labels are correct
assert_dbscan_equal(sk_labels, cu_labels, X,
cuml_dbscan.core_sample_indices_, eps)
def test_dbscan(datatype, nrows, ncols, max_mbytes_per_batch, out_dtype,
client):
from cuml.dask.cluster.dbscan import DBSCAN as cuDBSCAN
n_samples = nrows
n_feats = ncols
X, y = make_blobs(n_samples=n_samples,
cluster_std=0.01,
n_features=n_feats,
random_state=0)
eps = 1
cuml_dbscan = cuDBSCAN(eps=eps,
min_samples=2,
max_mbytes_per_batch=max_mbytes_per_batch,
output_type='numpy')
cu_labels = cuml_dbscan.fit_predict(X, out_dtype=out_dtype)
if nrows < 500000:
sk_dbscan = skDBSCAN(eps=1, min_samples=2, algorithm="brute")
sk_labels = sk_dbscan.fit_predict(X)
# Check the core points are equal
assert array_equal(cuml_dbscan.core_sample_indices_,
sk_dbscan.core_sample_indices_)
# Check the labels are correct
assert_dbscan_equal(sk_labels, cu_labels, X,
cuml_dbscan.core_sample_indices_, eps)
if out_dtype == "int32" or out_dtype == np.int32:
assert cu_labels.dtype == np.int32
elif out_dtype == "int64" or out_dtype == np.int64:
assert cu_labels.dtype == np.int64
def test_dbscan_precomputed(datatype, nrows, max_mbytes_per_batch, out_dtype,
client):
from cuml.dask.cluster.dbscan import DBSCAN as cuDBSCAN
# 2-dimensional dataset for easy distance matrix computation
X, y = make_blobs(n_samples=nrows,
cluster_std=0.01,
n_features=2,
random_state=0)
# Precompute distances
X_dist = pairwise_distances(X).astype(datatype)
eps = 1
cuml_dbscan = cuDBSCAN(eps=eps,
min_samples=2,
metric='precomputed',
max_mbytes_per_batch=max_mbytes_per_batch,
output_type='numpy')
cu_labels = cuml_dbscan.fit_predict(X_dist, out_dtype=out_dtype)
sk_dbscan = skDBSCAN(eps=eps,
min_samples=2,
metric='precomputed',
algorithm="brute")
sk_labels = sk_dbscan.fit_predict(X_dist)
# Check the core points are equal
assert array_equal(cuml_dbscan.core_sample_indices_,
sk_dbscan.core_sample_indices_)
# Check the labels are correct
assert_dbscan_equal(sk_labels, cu_labels, X,
cuml_dbscan.core_sample_indices_, eps)
def test_predict_large_n_classes(datatype):
nrows = 10000
ncols = 100
n_neighbors = 2
n_clusters = 1000
X, y = make_blobs(n_samples=nrows,
centers=n_clusters,
n_features=ncols,
cluster_std=0.01,
random_state=0)
X = X.astype(np.float32)
X_train, X_test, y_train, y_test = _build_train_test_data(X, y, datatype)
knn_cu = cuKNN(n_neighbors=n_neighbors)
knn_cu.fit(X_train, y_train)
y_hat = knn_cu.predict(X_test)
if datatype == "dataframe":
y_hat = y_hat.to_numpy()
y_test = y_test.to_numpy().ravel()
assert array_equal(y_hat.astype(np.int32), y_test.astype(np.int32))
def test_weighted_ridge(datatype, algorithm, fit_intercept,
normalize, distribution):
nrows, ncols, n_info = 1000, 20, 10
max_weight = 10
noise = 20
X_train, X_test, y_train, y_test = make_regression_dataset(
datatype, nrows, ncols, n_info, noise=noise
)
# set weight per sample to be from 1 to max_weight
if distribution == "uniform":
wt = np.random.randint(1, high=max_weight, size=len(X_train))
elif distribution == "exponential":
wt = np.random.exponential(size=len(X_train))
else:
wt = np.random.lognormal(size=len(X_train))
# Initialization of cuML's linear regression model
curidge = cuRidge(fit_intercept=fit_intercept,
normalize=normalize,
solver=algorithm)
# fit and predict cuml linear regression model
curidge.fit(X_train, y_train, sample_weight=wt)
curidge_predict = curidge.predict(X_test)
# sklearn linear regression model initialization, fit and predict
skridge = skRidge(fit_intercept=fit_intercept,
normalize=normalize)
skridge.fit(X_train, y_train, sample_weight=wt)
skridge_predict = skridge.predict(X_test)
assert array_equal(skridge_predict, curidge_predict, 1e-1, with_sign=True)
def test_umap_fit_transform_trust(name, target_metric):
if name == 'iris':
iris = datasets.load_iris()
data = iris.data
labels = iris.target
elif name == 'digits':
digits = datasets.load_digits(n_class=5)
data = digits.data
labels = digits.target
elif name == 'wine':
wine = datasets.load_wine()
data = wine.data
labels = wine.target
else:
data, labels = make_blobs(n_samples=500, n_features=10,
centers=10, random_state=42)
model = umap.UMAP(n_neighbors=10, min_dist=0.01,
target_metric=target_metric)
cuml_model = cuUMAP(n_neighbors=10, min_dist=0.01,
target_metric=target_metric)
embedding = model.fit_transform(data)
cuml_embedding = cuml_model.fit_transform(data, convert_dtype=True)
trust = trustworthiness(data, embedding, n_neighbors=10)
cuml_trust = trustworthiness(data, cuml_embedding, n_neighbors=10)
assert array_equal(trust, cuml_trust, 1e-1, with_sign=True)
def test_tsvd_fit_transform(datatype, name, use_handle):
if name == 'blobs':
X, y = make_blobs(n_samples=500000, n_features=1000, random_state=0)
elif name == 'random':
pytest.skip('fails when using random dataset '
'used by sklearn for testing')
shape = 5000, 100
rng = check_random_state(42)
X = rng.randint(-100, 20, np.product(shape)).reshape(shape)
else:
n, p = 500, 5
rng = np.random.RandomState(0)
X = rng.randn(n, p) * .1 + np.array([3, 4, 2, 3, 5])
if name != 'blobs':
skpca = skTSVD(n_components=1)
Xsktsvd = skpca.fit_transform(X)
handle, stream = get_handle(use_handle)
cutsvd = cuTSVD(n_components=1, handle=handle)
Xcutsvd = cutsvd.fit_transform(X)
cutsvd.handle.sync()
if name != 'blobs':
assert array_equal(Xcutsvd, Xsktsvd, 1e-3, with_sign=True)
def test_logistic_regression_decision_function(
dtype, nrows, column_info, num_classes, fit_intercept, sparse_input
):
ncols, n_info = column_info
X_train, X_test, y_train, y_test = make_classification_dataset(
datatype=dtype, nrows=nrows, ncols=ncols,
n_info=n_info, num_classes=num_classes
)
X_train = csr_matrix(X_train) if sparse_input else X_train
X_test = csr_matrix(X_test) if sparse_input else X_test
y_train = y_train.astype(dtype)
y_test = y_test.astype(dtype)
culog = cuLog(fit_intercept=fit_intercept, output_type="numpy")
culog.fit(X_train, y_train)
sklog = skLog(fit_intercept=fit_intercept)
sklog.coef_ = culog.coef_.T
if fit_intercept:
sklog.intercept_ = culog.intercept_
else:
skLog.intercept_ = 0
sklog.classes_ = np.arange(num_classes)
cu_dec_func = culog.decision_function(X_test)
if num_classes > 2:
cu_dec_func = cu_dec_func.T
sk_dec_func = sklog.decision_function(X_test)
assert array_equal(cu_dec_func, sk_dec_func)