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Reduce Naive Bayes test time #5082

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merged 4 commits into from
Dec 18, 2022
Merged

Reduce Naive Bayes test time #5082

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fbf3965
Reduce Naive Bayes test time
lowener Dec 13, 2022
f25c153
Fix style
lowener Dec 13, 2022
38153ce
Merge branch 'branch-23.02' into 23.02-tests-nb
dantegd Dec 17, 2022
2a37cba
Merge branch 'branch-23.02' into 23.02-tests-nb
lowener Dec 18, 2022
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121 changes: 26 additions & 95 deletions python/cuml/tests/test_naive_bayes.py
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Original file line number Diff line number Diff line change
Expand Up @@ -40,35 +40,6 @@
import numpy as np


@pytest.mark.parametrize("x_dtype", [cp.float32, cp.float64])
@pytest.mark.parametrize("y_dtype", [cp.int32, cp.int64])
def test_multinomial_basic_fit_predict_sparse(x_dtype, y_dtype, nlp_20news):
"""
Cupy Test
"""

X, y = nlp_20news

X = sparse_scipy_to_cp(X, x_dtype).astype(x_dtype)
y = y.astype(y_dtype)

# Priming it seems to lower the end-to-end runtime
model = MultinomialNB()
model.fit(X, y)

cp.cuda.Stream.null.synchronize()

model = MultinomialNB()
model.fit(X, y)

y_hat = model.predict(X)

y_hat = cp.asnumpy(y_hat)
y = cp.asnumpy(y)

assert accuracy_score(y, y_hat) >= 0.924


@pytest.mark.parametrize("x_dtype", [cp.int32, cp.int64])
@pytest.mark.parametrize("y_dtype", [cp.int32, cp.int64])
def test_sparse_integral_dtype_fails(x_dtype, y_dtype, nlp_20news):
Expand All @@ -77,7 +48,6 @@ def test_sparse_integral_dtype_fails(x_dtype, y_dtype, nlp_20news):
X = X.astype(x_dtype)
y = y.astype(y_dtype)

# Priming it seems to lower the end-to-end runtime
model = MultinomialNB()

with pytest.raises(ValueError):
Expand All @@ -102,8 +72,9 @@ def test_multinomial_basic_fit_predict_dense_numpy(x_dtype, y_dtype,
"""
X, y = nlp_20news
n_rows = 500
n_cols = 10000

X = sparse_scipy_to_cp(X, cp.float32).tocsr()[:n_rows]
X = sparse_scipy_to_cp(X, cp.float32).tocsr()[:n_rows, :n_cols]
y = y[:n_rows].astype(y_dtype)

model = MultinomialNB()
Expand Down Expand Up @@ -167,7 +138,7 @@ def test_multinomial_partial_fit(x_dtype, y_dtype, nlp_20news):

@pytest.mark.parametrize("x_dtype", [cp.float32, cp.float64])
@pytest.mark.parametrize("y_dtype", [cp.int32, cp.int64])
def test_multinomial_predict_proba(x_dtype, y_dtype, nlp_20news):
def test_multinomial(x_dtype, y_dtype, nlp_20news):

X, y = nlp_20news

Expand All @@ -182,67 +153,24 @@ def test_multinomial_predict_proba(x_dtype, y_dtype, nlp_20news):
sk_model = skNB()

cuml_model.fit(cu_X, cu_y)

sk_model.fit(X, y)

cuml_log_proba = cuml_model.predict_log_proba(cu_X).get()
sk_log_proba = sk_model.predict_log_proba(X)
cuml_proba = cuml_model.predict_proba(cu_X).get()
sk_proba = sk_model.predict_proba(X)

assert_allclose(cuml_proba, sk_proba, atol=1e-6, rtol=1e-2)


@pytest.mark.parametrize("x_dtype", [cp.float32, cp.float64])
@pytest.mark.parametrize("y_dtype", [cp.int32, cp.int64])
def test_multinomial_predict_log_proba(x_dtype, y_dtype, nlp_20news):

X, y = nlp_20news

cu_X = sparse_scipy_to_cp(X, x_dtype).astype(x_dtype)
cu_y = y.astype(y_dtype)

cu_X = cu_X.tocsr()

y = y.get()

cuml_model = MultinomialNB()
sk_model = skNB()

cuml_model.fit(cu_X, cu_y)

sk_model.fit(X, y)

cuml_proba = cuml_model.predict_log_proba(cu_X).get()
sk_proba = sk_model.predict_log_proba(X)

assert_allclose(cuml_proba, sk_proba, atol=1e-2, rtol=1e-2)


@pytest.mark.parametrize("x_dtype", [cp.float32, cp.float64])
@pytest.mark.parametrize("y_dtype", [cp.int32, cp.int64])
def test_multinomial_score(x_dtype, y_dtype, nlp_20news):

X, y = nlp_20news

cu_X = sparse_scipy_to_cp(X, x_dtype).astype(x_dtype)
cu_y = y.astype(y_dtype)

cu_X = cu_X.tocsr()

y = y.get()

cuml_model = MultinomialNB()
sk_model = skNB()

cuml_model.fit(cu_X, cu_y)

sk_model.fit(X, y)

cuml_score = cuml_model.score(cu_X, cu_y)
sk_score = sk_model.score(X, y)

THRES = 1e-4
y_hat = cuml_model.predict(cu_X)
y_hat = cp.asnumpy(y_hat)
cu_y = cp.asnumpy(cu_y)

THRES = 1e-4
assert_allclose(cuml_log_proba, sk_log_proba, atol=1e-2, rtol=1e-2)
assert_allclose(cuml_proba, sk_proba, atol=1e-6, rtol=1e-2)
assert sk_score - THRES <= cuml_score <= sk_score + THRES
assert accuracy_score(y, y_hat) >= 0.924


@pytest.mark.parametrize("x_dtype", [cp.float32, cp.float64])
Expand All @@ -251,11 +179,12 @@ def test_multinomial_score(x_dtype, y_dtype, nlp_20news):
def test_bernoulli(x_dtype, y_dtype, is_sparse, nlp_20news):
X, y = nlp_20news
n_rows = 500
n_cols = 20000

X = sparse_scipy_to_cp(X, x_dtype).astype(x_dtype)
y = y.astype(y_dtype)

X = X.tocsr()[:n_rows]
X = X.tocsr()[:n_rows, :n_cols]
y = y[:n_rows]
if not is_sparse:
X = X.todense()
Expand Down Expand Up @@ -330,11 +259,12 @@ def test_bernoulli_partial_fit(x_dtype, y_dtype, nlp_20news):
def test_complement(x_dtype, y_dtype, is_sparse, norm, nlp_20news):
X, y = nlp_20news
n_rows = 500
n_cols = 20000

X = sparse_scipy_to_cp(X, x_dtype).astype(x_dtype)
y = y.astype(y_dtype)

X = X.tocsr()[:n_rows]
X = X.tocsr()[:n_rows, :n_cols]
y = y[:n_rows]
if not is_sparse:
X = X.todense()
Expand Down Expand Up @@ -444,7 +374,7 @@ def test_gaussian_fit_predict(x_dtype, y_dtype, is_sparse,
X, y = nlp_20news
model = GaussianNB()
n_rows = 500
n_cols = int(2e5)
n_cols = 50000
X = sparse_scipy_to_cp(X, x_dtype)
X = X.tocsr()[:n_rows, :n_cols]

Expand All @@ -466,11 +396,12 @@ def test_gaussian_fit_predict(x_dtype, y_dtype, is_sparse,
def test_gaussian_partial_fit(nlp_20news):
chunk_size = 250
n_rows = 1500
n_cols = 60000
x_dtype, y_dtype = cp.float32, cp.int32

X, y = nlp_20news

X = sparse_scipy_to_cp(X, x_dtype).tocsr()[:n_rows]
X = sparse_scipy_to_cp(X, x_dtype).tocsr()[:n_rows, :n_cols]
y = y.astype(y_dtype)[:n_rows]

model = GaussianNB()
Expand Down Expand Up @@ -517,10 +448,11 @@ def test_gaussian_parameters(priors, var_smoothing, nlp_20news):
x_dtype = cp.float32
y_dtype = cp.int32
nrows = 150
ncols = 20000

X, y = nlp_20news

X = sparse_scipy_to_cp(X[:nrows], x_dtype).todense()
X = sparse_scipy_to_cp(X[:nrows], x_dtype).todense()[:, :ncols]
y = y.astype(y_dtype)[:nrows]

if priors == 'balanced':
Expand Down Expand Up @@ -550,8 +482,8 @@ def test_categorical(x_dtype, y_dtype, is_sparse, nlp_20news):
if x_dtype == cp.int32 and is_sparse:
pytest.skip("Sparse matrices with integers dtype are not supported")
X, y = nlp_20news
n_rows = 2000
n_cols = 500
n_rows = 500
n_cols = 400

X = sparse_scipy_to_cp(X, dtype=cp.float32)
X = X.tocsr()[:n_rows, :n_cols]
Expand Down Expand Up @@ -591,16 +523,15 @@ def test_categorical_partial_fit(x_dtype, y_dtype, is_sparse, nlp_20news):
n_rows = 5000
n_cols = 500
chunk_size = 1000
expected_score = 0.1040

X, y = nlp_20news

X = sparse_scipy_to_cp(X, 'float32').tocsr()[:n_rows]
X = sparse_scipy_to_cp(X, 'float32').tocsr()[:n_rows, :n_cols]
if is_sparse:
X.data = X.data.astype(x_dtype)
expected_score = 0.5414
else:
X = X[:, :n_cols].todense().astype(x_dtype)
expected_score = 0.1040
X = X.todense().astype(x_dtype)
y = y.astype(y_dtype)[:n_rows]

model = CategoricalNB()
Expand Down