def test_real_algos_runner(algo_name):
pair = algorithms.algorithm_by_name(algo_name)
if (algo_name == 'UMAP' and not has_umap()) or \
(algo_name == 'FIL' and not has_xgboost()):
pytest.xfail()
runner = AccuracyComparisonRunner([20], [5],
dataset_name='classification',
test_fraction=0.20)
results = runner.run(pair)[0]
print(results)
assert results["cuml_acc"] is not None
def all_algorithms():
"""Returns all defined AlgorithmPair objects"""
algorithms = [
AlgorithmPair(
sklearn.cluster.KMeans,
cuml.cluster.KMeans,
shared_args=dict(init="random", n_clusters=8, max_iter=300),
name="KMeans",
accepts_labels=False,
accuracy_function=metrics.homogeneity_score,
),
AlgorithmPair(
sklearn.decomposition.PCA,
cuml.PCA,
shared_args=dict(n_components=10),
name="PCA",
accepts_labels=False,
),
AlgorithmPair(
sklearn.decomposition.TruncatedSVD,
cuml.decomposition.tsvd.TruncatedSVD,
shared_args=dict(n_components=10),
name="tSVD",
accepts_labels=False,
),
AlgorithmPair(
sklearn.random_projection.GaussianRandomProjection,
cuml.random_projection.GaussianRandomProjection,
shared_args=dict(n_components="auto"),
name="GaussianRandomProjection",
bench_func=fit_transform,
accepts_labels=False,
),
AlgorithmPair(
sklearn.neighbors.NearestNeighbors,
cuml.neighbors.NearestNeighbors,
shared_args=dict(n_neighbors=1024),
cpu_args=dict(algorithm="brute", n_jobs=-1),
cuml_args={},
name="NearestNeighbors",
accepts_labels=False,
bench_func=fit_kneighbors,
),
AlgorithmPair(
sklearn.cluster.DBSCAN,
cuml.DBSCAN,
shared_args=dict(eps=3, min_samples=2),
cpu_args=dict(algorithm="brute"),
name="DBSCAN",
accepts_labels=False,
),
AlgorithmPair(
sklearn.linear_model.LinearRegression,
cuml.linear_model.LinearRegression,
shared_args={},
name="LinearRegression",
accepts_labels=True,
accuracy_function=metrics.r2_score,
),
AlgorithmPair(
sklearn.linear_model.ElasticNet,
cuml.linear_model.ElasticNet,
shared_args={
"alpha": 0.1,
"l1_ratio": 0.5
},
name="ElasticNet",
accepts_labels=True,
accuracy_function=metrics.r2_score,
),
AlgorithmPair(
sklearn.linear_model.Lasso,
cuml.linear_model.Lasso,
shared_args={},
name="Lasso",
accepts_labels=True,
accuracy_function=metrics.r2_score,
),
AlgorithmPair(
sklearn.linear_model.Ridge,
cuml.linear_model.Ridge,
shared_args={},
name="Ridge",
accepts_labels=True,
accuracy_function=metrics.r2_score,
),
AlgorithmPair(
sklearn.linear_model.LogisticRegression,
cuml.linear_model.LogisticRegression,
shared_args=dict(), # Use default solvers
name="LogisticRegression",
accepts_labels=True,
accuracy_function=metrics.accuracy_score,
),
AlgorithmPair(
sklearn.ensemble.RandomForestClassifier,
cuml.ensemble.RandomForestClassifier,
shared_args={
"max_features": 1.0,
"n_estimators": 10
},
name="RandomForestClassifier",
accepts_labels=True,
cpu_data_prep_hook=_labels_to_int_hook,
cuml_data_prep_hook=_labels_to_int_hook,
accuracy_function=metrics.accuracy_score,
),
AlgorithmPair(
sklearn.ensemble.RandomForestRegressor,
cuml.ensemble.RandomForestRegressor,
shared_args={
"max_features": 1.0,
"n_estimators": 10
},
name="RandomForestRegressor",
accepts_labels=True,
accuracy_function=metrics.r2_score,
),
AlgorithmPair(
sklearn.manifold.TSNE,
cuml.manifold.TSNE,
shared_args=dict(),
name="TSNE",
accepts_labels=False,
),
AlgorithmPair(
None,
cuml.linear_model.MBSGDClassifier,
shared_args={},
cuml_args=dict(eta0=0.005, epochs=100),
name="MBSGDClassifier",
accepts_labels=True,
accuracy_function=cuml.metrics.accuracy_score,
),
AlgorithmPair(
treelite if has_treelite() else None,
cuml.ForestInference,
shared_args=dict(num_rounds=100, max_depth=10),
cuml_args=dict(
fil_algo="AUTO",
output_class=False,
threshold=0.5,
storage_type="AUTO",
),
name="FIL",
accepts_labels=False,
setup_cpu_func=_build_treelite_classifier,
setup_cuml_func=_build_fil_classifier,
cpu_data_prep_hook=_treelite_format_hook,
accuracy_function=_treelite_fil_accuracy_score,
bench_func=predict,
),
AlgorithmPair(
treelite if has_treelite() else None,
cuml.ForestInference,
shared_args=dict(n_estimators=100, max_leaf_nodes=2**10),
cuml_args=dict(
fil_algo="AUTO",
output_class=False,
threshold=0.5,
storage_type="SPARSE",
),
name="Sparse-FIL-SKL",
accepts_labels=False,
setup_cpu_func=_build_cpu_skl_classifier,
setup_cuml_func=_build_fil_skl_classifier,
accuracy_function=_treelite_fil_accuracy_score,
bench_func=predict,
),
]
if has_umap():
algorithms.append(
AlgorithmPair(
umap.UMAP,
cuml.manifold.UMAP,
shared_args=dict(n_neighbors=5, n_epochs=500),
name="UMAP",
accepts_labels=False,
accuracy_function=cuml.metrics.trustworthiness,
))
return algorithms
fit_kneighbors,
fit_transform,
predict,
_build_cpu_skl_classifier,
_build_fil_skl_classifier,
_build_fil_classifier,
_build_treelite_classifier,
_treelite_fil_accuracy_score,
)
from cuml.utils.import_utils import has_treelite
if has_treelite():
import treelite
import treelite.runtime
if has_umap():
import umap
class AlgorithmPair:
"""
Wraps a cuML algorithm and (optionally) a cpu-based algorithm
(typically scikit-learn, but does not need to be as long as it offers
`fit` and `predict` or `transform` methods).
Provides mechanisms to run each version with default arguments.
If no CPU-based version of the algorithm is available, pass None for the
cpu_class when instantiating
Parameters
----------
cpu_class : class