[dask][tests] reduce code duplication in Dask tests (#3828)

tests/python_package_test/test_dask.py

@@ -133,19 +133,22 @@ def test_classifier(output, centers, client, listen_port):
  centers=centers
  )
 
+ params = {
+ "n_estimators": 10,
+ "num_leaves": 10
+ }
  dask_classifier = dlgbm.DaskLGBMClassifier(
  time_out=5,
  local_listen_port=listen_port,
- n_estimators=10,
- num_leaves=10
+ **params
  )
  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()
  s1 = accuracy_score(dy, p1)
  p1 = p1.compute()
 
- local_classifier = lightgbm.LGBMClassifier(n_estimators=10, num_leaves=10)
+ local_classifier = lightgbm.LGBMClassifier(**params)
  local_classifier.fit(X, y, sample_weight=w)
  p2 = local_classifier.predict(X)
  p2_proba = local_classifier.predict_proba(X)
@@ -169,20 +172,20 @@ def test_classifier_pred_contrib(output, centers, client, listen_port):
  centers=centers
  )
 
+ params = {
+ "n_estimators": 10,
+ "num_leaves": 10
+ }
  dask_classifier = dlgbm.DaskLGBMClassifier(
  time_out=5,
  local_listen_port=listen_port,
  tree_learner='data',
- n_estimators=10,
- num_leaves=10
+ **params
  )
  dask_classifier = dask_classifier.fit(dX, dy, sample_weight=dw, client=client)
  preds_with_contrib = dask_classifier.predict(dX, pred_contrib=True).compute()
 
- local_classifier = lightgbm.LGBMClassifier(
- n_estimators=10,
- num_leaves=10
- )
+ local_classifier = lightgbm.LGBMClassifier(**params)
  local_classifier.fit(X, y, sample_weight=w)
  local_preds_with_contrib = local_classifier.predict(X, pred_contrib=True)
 
@@ -243,16 +246,19 @@ def test_classifier_local_predict(client, listen_port):
  output='array'
  )
 
+ params = {
+ "n_estimators": 10,
+ "num_leaves": 10
+ }
  dask_classifier = dlgbm.DaskLGBMClassifier(
  time_out=5,
  local_port=listen_port,
- n_estimators=10,
- num_leaves=10
+ **params
  )
  dask_classifier = dask_classifier.fit(dX, dy, sample_weight=dw, client=client)
  p1 = dask_classifier.to_local().predict(dX)
 
- local_classifier = lightgbm.LGBMClassifier(n_estimators=10, num_leaves=10)
+ local_classifier = lightgbm.LGBMClassifier(**params)
  local_classifier.fit(X, y, sample_weight=w)
  p2 = local_classifier.predict(X)
 
@@ -270,20 +276,23 @@ def test_regressor(output, client, listen_port):
  output=output
  )
 
+ params = {
+ "random_state": 42,
+ "num_leaves": 10
+ }
  dask_regressor = dlgbm.DaskLGBMRegressor(
  time_out=5,
  local_listen_port=listen_port,
- seed=42,
- num_leaves=10,
- tree='data'
+ tree='data',
+ **params
  )
  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 = lightgbm.LGBMRegressor(**params)
  local_regressor.fit(X, y, sample_weight=w)
  s2 = local_regressor.score(X, y)
  p2 = local_regressor.predict(X)
@@ -306,20 +315,20 @@ def test_regressor_pred_contrib(output, client, listen_port):
  output=output
  )
 
+ params = {
+ "n_estimators": 10,
+ "num_leaves": 10
+ }
  dask_regressor = dlgbm.DaskLGBMRegressor(
  time_out=5,
  local_listen_port=listen_port,
  tree_learner='data',
- n_estimators=10,
- num_leaves=10
+ **params
  )
  dask_regressor = dask_regressor.fit(dX, dy, sample_weight=dw, client=client)
  preds_with_contrib = dask_regressor.predict(dX, pred_contrib=True).compute()
 
- local_regressor = lightgbm.LGBMRegressor(
- n_estimators=10,
- num_leaves=10
- )
+ local_regressor = lightgbm.LGBMRegressor(**params)
  local_regressor.fit(X, y, sample_weight=w)
  local_preds_with_contrib = local_regressor.predict(X, pred_contrib=True)
 
@@ -341,26 +350,23 @@ def test_regressor_quantile(output, client, listen_port, alpha):
  output=output
  )
 
+ params = {
+ "objective": "quantile",
+ "alpha": alpha,
+ "random_state": 42,
+ "n_estimators": 10,
+ "num_leaves": 10
+ }
  dask_regressor = dlgbm.DaskLGBMRegressor(
  local_listen_port=listen_port,
- seed=42,
- objective='quantile',
- alpha=alpha,
- n_estimators=10,
- num_leaves=10,
- tree_learner_type='data_parallel'
+ tree_learner_type='data_parallel',
+ **params
  )
  dask_regressor = dask_regressor.fit(dX, dy, client=client, sample_weight=dw)
  p1 = dask_regressor.predict(dX).compute()
  q1 = np.count_nonzero(y < p1) / y.shape[0]
 
- local_regressor = lightgbm.LGBMRegressor(
- seed=42,
- objective='quantile',
- alpha=alpha,
- n_estimatores=10,
- num_leaves=10
- )
+ local_regressor = lightgbm.LGBMRegressor(**params)
  local_regressor.fit(X, y, sample_weight=w)
  p2 = local_regressor.predict(X)
  q2 = np.count_nonzero(y < p2) / y.shape[0]
@@ -377,7 +383,7 @@ def test_regressor_local_predict(client, listen_port):
 
  dask_regressor = dlgbm.DaskLGBMRegressor(
  local_listen_port=listen_port,
- seed=42,
+ random_state=42,
  n_estimators=10,
  num_leaves=10,
  tree_type='data'
@@ -407,25 +413,23 @@ def test_ranker(output, client, listen_port, group):
 
  # use many trees + leaves to overfit, help ensure that dask data-parallel strategy matches that of
  # serial learner. See https://github.com/microsoft/LightGBM/issues/3292#issuecomment-671288210.
+ params = {
+ "random_state": 42,
+ "n_estimators": 50,
+ "num_leaves": 20,
+ "min_child_samples": 1
+ }
  dask_ranker = dlgbm.DaskLGBMRanker(
  time_out=5,
  local_listen_port=listen_port,
  tree_learner_type='data_parallel',
- n_estimators=50,
- num_leaves=20,
- seed=42,
- min_child_samples=1
+ **params
  )
  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()
 
- local_ranker = lightgbm.LGBMRanker(
- n_estimators=50,
- num_leaves=20,
- seed=42,
- min_child_samples=1
- )
+ local_ranker = lightgbm.LGBMRanker(**params)
  local_ranker.fit(X, y, sample_weight=w, group=g)
  rnkvec_local = local_ranker.predict(X)
 
@@ -453,7 +457,7 @@ def test_ranker_local_predict(output, client, listen_port, group):
  tree_learner='data',
  n_estimators=10,
  num_leaves=10,
- seed=42,
+ random_state=42,
  min_child_samples=1
  )
  dask_ranker = dask_ranker.fit(dX, dy, group=dg, client=client)