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Pytest updates for Scikit-learn 0.24 (#4205)
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Authors:
  - Dante Gama Dessavre (https://github.com/dantegd)

Approvers:
  - Victor Lafargue (https://github.com/viclafargue)
  - Corey J. Nolet (https://github.com/cjnolet)

URL: #4205
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dantegd authored Sep 13, 2021
1 parent 36b3746 commit b459603
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Showing 5 changed files with 5 additions and 5 deletions.
2 changes: 1 addition & 1 deletion python/cuml/test/test_metrics.py
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Expand Up @@ -152,7 +152,7 @@ def test_sklearn_search():
normalize=normalize, solver="eig")

assert getattr(cu_clf, 'score', False)
sk_cu_grid = GridSearchCV(cu_clf, params, cv=5, iid=False)
sk_cu_grid = GridSearchCV(cu_clf, params, cv=5)

gdf_data = cudf.DataFrame(X_train)
gdf_train = cudf.DataFrame(dict(train=y_train))
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2 changes: 1 addition & 1 deletion python/cuml/test/test_pickle.py
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Expand Up @@ -24,7 +24,7 @@
from cuml.test.test_svm import compare_svm, compare_probabilistic_svm
from sklearn.base import clone
from sklearn.datasets import load_iris, make_classification, make_regression
from sklearn.manifold.t_sne import trustworthiness
from sklearn.manifold import trustworthiness
from sklearn.model_selection import train_test_split


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2 changes: 1 addition & 1 deletion python/cuml/test/test_trustworthiness.py
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Expand Up @@ -13,7 +13,7 @@
# limitations under the License.

import pytest
from sklearn.manifold.t_sne import trustworthiness as sklearn_trustworthiness
from sklearn.manifold import trustworthiness as sklearn_trustworthiness
from cuml.metrics import trustworthiness as cuml_trustworthiness

from sklearn.datasets import make_blobs
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2 changes: 1 addition & 1 deletion python/cuml/test/test_tsne.py
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Expand Up @@ -23,7 +23,7 @@
from cuml.neighbors import NearestNeighbors as cuKNN

from sklearn.datasets import make_blobs
from sklearn.manifold.t_sne import trustworthiness
from sklearn.manifold import trustworthiness
from sklearn import datasets


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2 changes: 1 addition & 1 deletion python/cuml/test/test_umap.py
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Expand Up @@ -37,7 +37,7 @@
from sklearn import datasets
from sklearn.cluster import KMeans
from sklearn.datasets import make_blobs
from sklearn.manifold.t_sne import trustworthiness
from sklearn.manifold import trustworthiness
from sklearn.metrics import adjusted_rand_score

dataset_names = ['iris', 'digits', 'wine', 'blobs']
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