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[dask] merge local_predict tests into other tests (fixes #3833) #3842

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merged 6 commits into from
Jan 25, 2021
Merged

[dask] merge local_predict tests into other tests (fixes #3833) #3842

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3eb3605
Merge test_<est>_local_predict and test_<est> tests for Dask module #…
Jan 24, 2021
a4b56c5
Merge test_<est>_local_predict to test_<est> tests in dask module - r…
Jan 24, 2021
9c7617a
Update test_classifier and rename variables resolves #3833
Jan 25, 2021
97d2e8d
rename variables resolves #3833
Jan 25, 2021
b5e26d9
Merge branch 'master' into reduce-tests
jameslamb Jan 25, 2021
c2582a5
manage precision error #3833
Jan 25, 2021
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93 changes: 10 additions & 83 deletions tests/python_package_test/test_dask.py
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Original file line number Diff line number Diff line change
Expand Up @@ -142,9 +142,11 @@ def test_classifier(output, centers, client, listen_port):
local_listen_port=listen_port,
**params
)

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Suggested change

please revert this unrelated whitespace change

dask_classifier = dask_classifier.fit(dX, dy, sample_weight=dw, client=client)
p1 = dask_classifier.predict(dX)
p1_proba = dask_classifier.predict_proba(dX).compute()
p1_local = dask_classifier.to_local().predict(X)
s1 = accuracy_score(dy, p1)
p1 = p1.compute()

Expand All @@ -153,12 +155,13 @@ def test_classifier(output, centers, client, listen_port):
p2 = local_classifier.predict(X)
p2_proba = local_classifier.predict_proba(X)
s2 = local_classifier.score(X, y)

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please revert this unrelated whitespace change and add this line back

assert_eq(s1, s2)
assert_eq(p1, p2)
assert_eq(y, p1)
assert_eq(y, p2)
assert_eq(p1_proba, p2_proba, atol=0.3)
assert_eq(p1_local, p2)
assert_eq(y, p1_local)

client.close()

Expand Down Expand Up @@ -240,35 +243,6 @@ def test_training_does_not_fail_on_port_conflicts(client):
client.close()


def test_classifier_local_predict(client, listen_port):
X, y, w, dX, dy, dw = _create_data(
objective='classification',
output='array'
)

params = {
"n_estimators": 10,
"num_leaves": 10
}
dask_classifier = dlgbm.DaskLGBMClassifier(
time_out=5,
local_port=listen_port,
**params
)
dask_classifier = dask_classifier.fit(dX, dy, sample_weight=dw, client=client)
p1 = dask_classifier.to_local().predict(dX)

local_classifier = lightgbm.LGBMClassifier(**params)
local_classifier.fit(X, y, sample_weight=w)
p2 = local_classifier.predict(X)

assert_eq(p1, p2)
assert_eq(y, p1)
assert_eq(y, p2)

client.close()


@pytest.mark.parametrize('output', data_output)
def test_regressor(output, client, listen_port):
X, y, w, dX, dy, dw = _create_data(
Expand All @@ -291,6 +265,8 @@ def test_regressor(output, client, listen_port):
if output != 'dataframe':
s1 = r2_score(dy, p1)
p1 = p1.compute()
p2_local = dask_regressor.to_local().predict(X)
s2_local = dask_regressor.to_local().score(X, y)
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Suggested change
p2_local = dask_regressor.to_local().predict(X)
s2_local = dask_regressor.to_local().score(X, y)
p1_local = dask_regressor.to_local().predict(X)
s1_local = dask_regressor.to_local().score(X, y)

Please call these p1_local and p2_local. We have a convention in these tests right now that p1 references predictions from a model trained with DaskLGBMRegressor and the p2 references things from lightgbm.sklearn.LGBMRegressor. I know that's confusing and we should probably change it, but for now this test should be consistent with the existing code.


local_regressor = lightgbm.LGBMRegressor(**params)
local_regressor.fit(X, y, sample_weight=w)
Expand All @@ -300,10 +276,12 @@ def test_regressor(output, client, listen_port):
# Scores should be the same
if output != 'dataframe':
assert_eq(s1, s2, atol=.01)
assert_eq(s1, s2_local)
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Suggested change
assert_eq(s1, s2_local)
assert_eq(s1, s1_local)


# Predictions should be roughly the same
assert_eq(y, p1, rtol=1., atol=100.)
assert_eq(y, p2, rtol=1., atol=50.)
assert_eq(p1, p2_local)
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Suggested change
assert_eq(p1, p2_local)
assert_eq(p1, p1_local)


client.close()

Expand Down Expand Up @@ -378,30 +356,6 @@ def test_regressor_quantile(output, client, listen_port, alpha):
client.close()


def test_regressor_local_predict(client, listen_port):
X, y, _, dX, dy, dw = _create_data('regression', output='array')

dask_regressor = dlgbm.DaskLGBMRegressor(
local_listen_port=listen_port,
random_state=42,
n_estimators=10,
num_leaves=10,
tree_type='data'
)
dask_regressor = dask_regressor.fit(dX, dy, sample_weight=dw, client=client)
p1 = dask_regressor.predict(dX)
p2 = dask_regressor.to_local().predict(X)
s1 = r2_score(dy, p1)
p1 = p1.compute()
s2 = dask_regressor.to_local().score(X, y)

# Predictions and scores should be the same
assert_eq(p1, p2)
assert_eq(s1, s2)

client.close()


@pytest.mark.parametrize('output', ['array', 'dataframe'])
@pytest.mark.parametrize('group', [None, group_sizes])
def test_ranker(output, client, listen_port, group):
Expand All @@ -428,6 +382,7 @@ def test_ranker(output, client, listen_port, group):
dask_ranker = dask_ranker.fit(dX, dy, sample_weight=dw, group=dg, client=client)
rnkvec_dask = dask_ranker.predict(dX)
rnkvec_dask = rnkvec_dask.compute()
rnkvec_local_1 = dask_ranker.to_local().predict(X)
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Suggested change
rnkvec_local_1 = dask_ranker.to_local().predict(X)
rnkvec_dask_local = dask_ranker.to_local().predict(X)

Please change this name so it's clearer how it differs from rnkvec_local


local_ranker = lightgbm.LGBMRanker(**params)
local_ranker.fit(X, y, sample_weight=w, group=g)
Expand All @@ -438,35 +393,7 @@ def test_ranker(output, client, listen_port, group):
dcor = spearmanr(rnkvec_dask, y).correlation
assert dcor > 0.6
assert spearmanr(rnkvec_dask, rnkvec_local).correlation > 0.75

client.close()


@pytest.mark.parametrize('output', ['array', 'dataframe'])
@pytest.mark.parametrize('group', [None, group_sizes])
def test_ranker_local_predict(output, client, listen_port, group):

X, y, w, g, dX, dy, dw, dg = _create_ranking_data(
output=output,
group=group
)

dask_ranker = dlgbm.DaskLGBMRanker(
time_out=5,
local_listen_port=listen_port,
tree_learner='data',
n_estimators=10,
num_leaves=10,
random_state=42,
min_child_samples=1
)
dask_ranker = dask_ranker.fit(dX, dy, group=dg, client=client)
rnkvec_dask = dask_ranker.predict(dX)
rnkvec_dask = rnkvec_dask.compute()
rnkvec_local = dask_ranker.to_local().predict(X)

# distributed and to-local scores should be the same.
assert_eq(rnkvec_dask, rnkvec_local)
assert_eq(rnkvec_dask, rnkvec_local_1)
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Suggested change
assert_eq(rnkvec_dask, rnkvec_local_1)
assert_eq(rnkvec_dask, rnkvec_dask_local)


client.close()

Expand Down