def test_regressor(output, client, listen_port):
X, y, w, dX, dy, dw = _create_data(objective='regression', output=output)
dask_regressor = dlgbm.DaskLGBMRegressor(time_out=5,
local_listen_port=listen_port,
seed=42,
num_leaves=10,
tree='data')
dask_regressor = dask_regressor.fit(dX,
dy,
client=client,
sample_weight=dw)
p1 = dask_regressor.predict(dX)
if output != 'dataframe':
s1 = r2_score(dy, p1)
p1 = p1.compute()
local_regressor = lightgbm.LGBMRegressor(seed=42, num_leaves=10)
local_regressor.fit(X, y, sample_weight=w)
s2 = local_regressor.score(X, y)
p2 = local_regressor.predict(X)
# Scores should be the same
if output != 'dataframe':
assert_eq(s1, s2, atol=.01)
# Predictions should be roughly the same
assert_eq(y, p1, rtol=1., atol=100.)
assert_eq(y, p2, rtol=1., atol=50.)
client.close()
def test_regressor_local_predict(client, listen_port): # noqa
X, y, w, dX, dy, dw = _create_data('regression', output='array')
a = dlgbm.LGBMRegressor(local_listen_port=listen_port, seed=42)
a = a.fit(dX, dy, sample_weight=dw, client=client)
p1 = a.predict(dX)
p2 = a.to_local().predict(X)
s1 = r2_score(dy, p1)
p1 = p1.compute()
s2 = a.to_local().score(X, y)
print(s1)
# Predictions and scores should be the same
assert_eq(p1, p2)
np.isclose(s1, s2)
def test_regressor_local_predict(loop, listen_port):
with cluster() as (s, [a, b]):
with Client(s['address'], loop=loop):
X, y, w, dX, dy, dw = _create_data('regression', output="array")
a = dlgbm.LGBMRegressor(local_listen_port=listen_port, seed=42)
a = a.fit(dX, dy, sample_weight=dw)
p1 = a.predict(dX)
p2 = a.to_local().predict(X)
s1 = r2_score(dy, p1)
p1 = p1.compute()
s2 = a.to_local().score(X, y)
print(s1)
# Predictions and scores should be the same
assert_eq(p1, p2)
np.isclose(s1, s2)
def test_regressor_local_predict(client, listen_port):
X, y, w, dX, dy, dw = _create_data('regression', output='array')
dask_regressor = dlgbm.DaskLGBMRegressor(local_listen_port=listen_port,
seed=42)
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)
def test_regressor(output, client, listen_port): # noqa
X, y, w, dX, dy, dw = _create_data('regression', output=output)
a = dlgbm.LGBMRegressor(time_out=5, local_listen_port=listen_port, seed=42)
a = a.fit(dX, dy, client=client, sample_weight=dw)
p1 = a.predict(dX, client=client)
if output != 'dataframe':
s1 = r2_score(dy, p1)
p1 = p1.compute()
b = lightgbm.LGBMRegressor(seed=42)
b.fit(X, y, sample_weight=w)
s2 = b.score(X, y)
p2 = b.predict(X)
# Scores should be the same
if output != 'dataframe':
assert_eq(s1, s2, atol=.01)
# Predictions should be roughly the same
assert_eq(y, p1, rtol=1., atol=50.)
assert_eq(y, p2, rtol=1., atol=50.)
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()