test_dask.py

# coding: utf-8
"""Tests for lightgbm.dask module"""

import inspect
import pickle
import socket
from itertools import groupby
from os import getenv
from sys import platform

import lightgbm as lgb
import pytest
if not platform.startswith('linux'):
    pytest.skip('lightgbm.dask is currently supported in Linux environments', allow_module_level=True)
if not lgb.compat.DASK_INSTALLED:
    pytest.skip('Dask is not installed', allow_module_level=True)

import cloudpickle
import dask.array as da
import dask.dataframe as dd
import joblib
import numpy as np
import pandas as pd
from scipy.stats import spearmanr
from dask.array.utils import assert_eq
from dask.distributed import default_client, Client, LocalCluster, wait
from distributed.utils_test import client, cluster_fixture, gen_cluster, loop
from scipy.sparse import csr_matrix
from sklearn.datasets import make_blobs, make_regression

from .utils import make_ranking


# time, in seconds, to wait for the Dask client to close. Used to avoid teardown errors
# see https://distributed.dask.org/en/latest/api.html#distributed.Client.close
CLIENT_CLOSE_TIMEOUT = 120

data_output = ['array', 'scipy_csr_matrix', 'dataframe', 'dataframe-with-categorical']
data_centers = [[[-4, -4], [4, 4]], [[-4, -4], [4, 4], [-4, 4]]]
group_sizes = [5, 5, 5, 10, 10, 10, 20, 20, 20, 50, 50]

pytestmark = [
    pytest.mark.skipif(getenv('TASK', '') == 'mpi', reason='Fails to run with MPI interface'),
    pytest.mark.skipif(getenv('TASK', '') == 'gpu', reason='Fails to run with GPU interface')
]


@pytest.fixture()
def listen_port():
    listen_port.port += 10
    return listen_port.port


listen_port.port = 13000


def _create_ranking_data(n_samples=100, output='array', chunk_size=50, **kwargs):
    X, y, g = make_ranking(n_samples=n_samples, random_state=42, **kwargs)
    rnd = np.random.RandomState(42)
    w = rnd.rand(X.shape[0]) * 0.01
    g_rle = np.array([len(list(grp)) for _, grp in groupby(g)])

    if output.startswith('dataframe'):
        # add target, weight, and group to DataFrame so that partitions abide by group boundaries.
        X_df = pd.DataFrame(X, columns=[f'feature_{i}' for i in range(X.shape[1])])
        if output == 'dataframe-with-categorical':
            for i in range(5):
                col_name = "cat_col" + str(i)
                cat_values = rnd.choice(['a', 'b'], X.shape[0])
                cat_series = pd.Series(
                    cat_values,
                    dtype='category'
                )
                X_df[col_name] = cat_series
        X = X_df.copy()
        X_df = X_df.assign(y=y, g=g, w=w)

        # set_index ensures partitions are based on group id.
        # See https://stackoverflow.com/questions/49532824/dask-dataframe-split-partitions-based-on-a-column-or-function.
        X_df.set_index('g', inplace=True)
        dX = dd.from_pandas(X_df, chunksize=chunk_size)

        # separate target, weight from features.
        dy = dX['y']
        dw = dX['w']
        dX = dX.drop(columns=['y', 'w'])
        dg = dX.index.to_series()

        # encode group identifiers into run-length encoding, the format LightGBMRanker is expecting
        # so that within each partition, sum(g) = n_samples.
        dg = dg.map_partitions(lambda p: p.groupby('g', sort=False).apply(lambda z: z.shape[0]))
    elif output == 'array':
        # ranking arrays: one chunk per group. Each chunk must include all columns.
        p = X.shape[1]
        dX, dy, dw, dg = [], [], [], []
        for g_idx, rhs in enumerate(np.cumsum(g_rle)):
            lhs = rhs - g_rle[g_idx]
            dX.append(da.from_array(X[lhs:rhs, :], chunks=(rhs - lhs, p)))
            dy.append(da.from_array(y[lhs:rhs]))
            dw.append(da.from_array(w[lhs:rhs]))
            dg.append(da.from_array(np.array([g_rle[g_idx]])))

        dX = da.concatenate(dX, axis=0)
        dy = da.concatenate(dy, axis=0)
        dw = da.concatenate(dw, axis=0)
        dg = da.concatenate(dg, axis=0)
    else:
        raise ValueError('Ranking data creation only supported for Dask arrays and dataframes')

    return X, y, w, g_rle, dX, dy, dw, dg


def _create_data(objective, n_samples=100, centers=2, output='array', chunk_size=50):
    if objective == 'classification':
        X, y = make_blobs(n_samples=n_samples, centers=centers, random_state=42)
    elif objective == 'regression':
        X, y = make_regression(n_samples=n_samples, random_state=42)
    else:
        raise ValueError("Unknown objective '%s'" % objective)
    rnd = np.random.RandomState(42)
    weights = rnd.random(X.shape[0]) * 0.01

    if output == 'array':
        dX = da.from_array(X, (chunk_size, X.shape[1]))
        dy = da.from_array(y, chunk_size)
        dw = da.from_array(weights, chunk_size)
    elif output.startswith('dataframe'):
        X_df = pd.DataFrame(X, columns=['feature_%d' % i for i in range(X.shape[1])])
        if output == 'dataframe-with-categorical':
            num_cat_cols = 5
            for i in range(num_cat_cols):
                col_name = "cat_col" + str(i)
                cat_values = rnd.choice(['a', 'b'], X.shape[0])
                cat_series = pd.Series(
                    cat_values,
                    dtype='category'
                )
                X_df[col_name] = cat_series
                X = np.hstack((X, cat_series.cat.codes.values.reshape(-1, 1)))

            # for the small data sizes used in tests, it's hard to get LGBMRegressor to choose
            # categorical features for splits. So for regression tests with categorical features,
            # _create_data() returns a DataFrame with ONLY categorical features
            if objective == 'regression':
                cat_cols = [col for col in X_df.columns if col.startswith('cat_col')]
                X_df = X_df[cat_cols]
                X = X[:, -num_cat_cols:]
        y_df = pd.Series(y, name='target')
        dX = dd.from_pandas(X_df, chunksize=chunk_size)
        dy = dd.from_pandas(y_df, chunksize=chunk_size)
        dw = dd.from_array(weights, chunksize=chunk_size)
    elif output == 'scipy_csr_matrix':
        dX = da.from_array(X, chunks=(chunk_size, X.shape[1])).map_blocks(csr_matrix)
        dy = da.from_array(y, chunks=chunk_size)
        dw = da.from_array(weights, chunk_size)
    else:
        raise ValueError("Unknown output type '%s'" % output)

    return X, y, weights, dX, dy, dw


def _r2_score(dy_true, dy_pred):
    numerator = ((dy_true - dy_pred) ** 2).sum(axis=0, dtype=np.float64)
    denominator = ((dy_true - dy_pred.mean(axis=0)) ** 2).sum(axis=0, dtype=np.float64)
    return (1 - numerator / denominator).compute()


def _accuracy_score(dy_true, dy_pred):
    return da.average(dy_true == dy_pred).compute()


def _pickle(obj, filepath, serializer):
    if serializer == 'pickle':
        with open(filepath, 'wb') as f:
            pickle.dump(obj, f)
    elif serializer == 'joblib':
        joblib.dump(obj, filepath)
    elif serializer == 'cloudpickle':
        with open(filepath, 'wb') as f:
            cloudpickle.dump(obj, f)
    else:
        raise ValueError(f'Unrecognized serializer type: {serializer}')


def _unpickle(filepath, serializer):
    if serializer == 'pickle':
        with open(filepath, 'rb') as f:
            return pickle.load(f)
    elif serializer == 'joblib':
        return joblib.load(filepath)
    elif serializer == 'cloudpickle':
        with open(filepath, 'rb') as f:
            return cloudpickle.load(f)
    else:
        raise ValueError(f'Unrecognized serializer type: {serializer}')


@pytest.mark.parametrize('output', data_output)
@pytest.mark.parametrize('centers', data_centers)
def test_classifier(output, centers, client, listen_port):
    X, y, w, dX, dy, dw = _create_data(
        objective='classification',
        output=output,
        centers=centers
    )

    params = {
        "n_estimators": 10,
        "num_leaves": 10
    }

    if output == 'dataframe-with-categorical':
        params["categorical_feature"] = [
            i for i, col in enumerate(dX.columns) if col.startswith('cat_')
        ]

    dask_classifier = lgb.DaskLGBMClassifier(
        client=client,
        time_out=5,
        local_listen_port=listen_port,
        **params
    )
    dask_classifier = dask_classifier.fit(dX, dy, sample_weight=dw)
    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()

    local_classifier = lgb.LGBMClassifier(**params)
    local_classifier.fit(X, y, sample_weight=w)
    p2 = local_classifier.predict(X)
    p2_proba = local_classifier.predict_proba(X)
    s2 = local_classifier.score(X, y)

    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)

    # be sure LightGBM actually used at least one categorical column,
    # and that it was correctly treated as a categorical feature
    if output == 'dataframe-with-categorical':
        cat_cols = [
            col for col in dX.columns
            if dX.dtypes[col].name == 'category'
        ]
        tree_df = dask_classifier.booster_.trees_to_dataframe()
        node_uses_cat_col = tree_df['split_feature'].isin(cat_cols)
        assert node_uses_cat_col.sum() > 0
        assert tree_df.loc[node_uses_cat_col, "decision_type"].unique()[0] == '=='

    client.close(timeout=CLIENT_CLOSE_TIMEOUT)


@pytest.mark.parametrize('output', data_output)
@pytest.mark.parametrize('centers', data_centers)
def test_classifier_pred_contrib(output, centers, client, listen_port):
    X, y, w, dX, dy, dw = _create_data(
        objective='classification',
        output=output,
        centers=centers
    )

    params = {
        "n_estimators": 10,
        "num_leaves": 10
    }

    if output == 'dataframe-with-categorical':
        params["categorical_feature"] = [
            i for i, col in enumerate(dX.columns) if col.startswith('cat_')
        ]

    dask_classifier = lgb.DaskLGBMClassifier(
        client=client,
        time_out=5,
        local_listen_port=listen_port,
        tree_learner='data',
        **params
    )
    dask_classifier = dask_classifier.fit(dX, dy, sample_weight=dw)
    preds_with_contrib = dask_classifier.predict(dX, pred_contrib=True).compute()

    local_classifier = lgb.LGBMClassifier(**params)
    local_classifier.fit(X, y, sample_weight=w)
    local_preds_with_contrib = local_classifier.predict(X, pred_contrib=True)

    if output == 'scipy_csr_matrix':
        preds_with_contrib = np.array(preds_with_contrib.todense())

    # be sure LightGBM actually used at least one categorical column,
    # and that it was correctly treated as a categorical feature
    if output == 'dataframe-with-categorical':
        cat_cols = [
            col for col in dX.columns
            if dX.dtypes[col].name == 'category'
        ]
        tree_df = dask_classifier.booster_.trees_to_dataframe()
        node_uses_cat_col = tree_df['split_feature'].isin(cat_cols)
        assert node_uses_cat_col.sum() > 0
        assert tree_df.loc[node_uses_cat_col, "decision_type"].unique()[0] == '=='

    # shape depends on whether it is binary or multiclass classification
    num_features = dask_classifier.n_features_
    num_classes = dask_classifier.n_classes_
    if num_classes == 2:
        expected_num_cols = num_features + 1
    else:
        expected_num_cols = (num_features + 1) * num_classes

    # * shape depends on whether it is binary or multiclass classification
    # * matrix for binary classification is of the form [feature_contrib, base_value],
    #   for multi-class it's [feat_contrib_class1, base_value_class1, feat_contrib_class2, base_value_class2, etc.]
    # * contrib outputs for distributed training are different than from local training, so we can just test
    #   that the output has the right shape and base values are in the right position
    assert preds_with_contrib.shape[1] == expected_num_cols
    assert preds_with_contrib.shape == local_preds_with_contrib.shape

    if num_classes == 2:
        assert len(np.unique(preds_with_contrib[:, num_features]) == 1)
    else:
        for i in range(num_classes):
            base_value_col = num_features * (i + 1) + i
            assert len(np.unique(preds_with_contrib[:, base_value_col]) == 1)

    client.close(timeout=CLIENT_CLOSE_TIMEOUT)


def test_training_does_not_fail_on_port_conflicts(client):
    _, _, _, dX, dy, dw = _create_data('classification', output='array')

    with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
        s.bind(('127.0.0.1', 12400))

        dask_classifier = lgb.DaskLGBMClassifier(
            client=client,
            time_out=5,
            local_listen_port=12400,
            n_estimators=5,
            num_leaves=5
        )
        for _ in range(5):
            dask_classifier.fit(
                X=dX,
                y=dy,
                sample_weight=dw,
            )
            assert dask_classifier.booster_

    client.close(timeout=CLIENT_CLOSE_TIMEOUT)


@pytest.mark.parametrize('output', data_output)
def test_regressor(output, client, listen_port):
    X, y, w, dX, dy, dw = _create_data(
        objective='regression',
        output=output
    )

    params = {
        "random_state": 42,
        "num_leaves": 10
    }

    if output == 'dataframe-with-categorical':
        params["categorical_feature"] = [
            i for i, col in enumerate(dX.columns) if col.startswith('cat_')
        ]

    dask_regressor = lgb.DaskLGBMRegressor(
        client=client,
        time_out=5,
        local_listen_port=listen_port,
        tree='data',
        **params
    )
    dask_regressor = dask_regressor.fit(dX, dy, sample_weight=dw)
    p1 = dask_regressor.predict(dX)
    if not output.startswith('dataframe'):
        s1 = _r2_score(dy, p1)
    p1 = p1.compute()
    p1_local = dask_regressor.to_local().predict(X)
    s1_local = dask_regressor.to_local().score(X, y)

    local_regressor = lgb.LGBMRegressor(**params)
    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 not output.startswith('dataframe'):
        assert_eq(s1, s2, atol=.01)
        assert_eq(s1, s1_local, atol=.003)

    # Predictions should be roughly the same.
    assert_eq(p1, p1_local)

    # The checks below are skipped
    # for the categorical data case because it's difficult to get
    # a good fit from just categoricals for a regression problem
    # with small data
    if output != 'dataframe-with-categorical':
        assert_eq(y, p1, rtol=1., atol=100.)
        assert_eq(y, p2, rtol=1., atol=50.)

    # be sure LightGBM actually used at least one categorical column,
    # and that it was correctly treated as a categorical feature
    if output == 'dataframe-with-categorical':
        cat_cols = [
            col for col in dX.columns
            if dX.dtypes[col].name == 'category'
        ]
        tree_df = dask_regressor.booster_.trees_to_dataframe()
        node_uses_cat_col = tree_df['split_feature'].isin(cat_cols)
        assert node_uses_cat_col.sum() > 0
        assert tree_df.loc[node_uses_cat_col, "decision_type"].unique()[0] == '=='

    client.close(timeout=CLIENT_CLOSE_TIMEOUT)


@pytest.mark.parametrize('output', data_output)
def test_regressor_pred_contrib(output, client, listen_port):
    X, y, w, dX, dy, dw = _create_data(
        objective='regression',
        output=output
    )

    params = {
        "n_estimators": 10,
        "num_leaves": 10
    }

    if output == 'dataframe-with-categorical':
        params["categorical_feature"] = [
            i for i, col in enumerate(dX.columns) if col.startswith('cat_')
        ]

    dask_regressor = lgb.DaskLGBMRegressor(
        client=client,
        time_out=5,
        local_listen_port=listen_port,
        tree_learner='data',
        **params
    )
    dask_regressor = dask_regressor.fit(dX, dy, sample_weight=dw)
    preds_with_contrib = dask_regressor.predict(dX, pred_contrib=True).compute()

    local_regressor = lgb.LGBMRegressor(**params)
    local_regressor.fit(X, y, sample_weight=w)
    local_preds_with_contrib = local_regressor.predict(X, pred_contrib=True)

    if output == "scipy_csr_matrix":
        preds_with_contrib = np.array(preds_with_contrib.todense())

    # contrib outputs for distributed training are different than from local training, so we can just test
    # that the output has the right shape and base values are in the right position
    num_features = dX.shape[1]
    assert preds_with_contrib.shape[1] == num_features + 1
    assert preds_with_contrib.shape == local_preds_with_contrib.shape

    # be sure LightGBM actually used at least one categorical column,
    # and that it was correctly treated as a categorical feature
    if output == 'dataframe-with-categorical':
        cat_cols = [
            col for col in dX.columns
            if dX.dtypes[col].name == 'category'
        ]
        tree_df = dask_regressor.booster_.trees_to_dataframe()
        node_uses_cat_col = tree_df['split_feature'].isin(cat_cols)
        assert node_uses_cat_col.sum() > 0
        assert tree_df.loc[node_uses_cat_col, "decision_type"].unique()[0] == '=='

    client.close(timeout=CLIENT_CLOSE_TIMEOUT)


@pytest.mark.parametrize('output', data_output)
@pytest.mark.parametrize('alpha', [.1, .5, .9])
def test_regressor_quantile(output, client, listen_port, alpha):
    X, y, w, dX, dy, dw = _create_data(
        objective='regression',
        output=output
    )

    params = {
        "objective": "quantile",
        "alpha": alpha,
        "random_state": 42,
        "n_estimators": 10,
        "num_leaves": 10
    }

    if output == 'dataframe-with-categorical':
        params["categorical_feature"] = [
            i for i, col in enumerate(dX.columns) if col.startswith('cat_')
        ]

    dask_regressor = lgb.DaskLGBMRegressor(
        client=client,
        local_listen_port=listen_port,
        tree_learner_type='data_parallel',
        **params
    )
    dask_regressor = dask_regressor.fit(dX, dy, sample_weight=dw)
    p1 = dask_regressor.predict(dX).compute()
    q1 = np.count_nonzero(y < p1) / y.shape[0]

    local_regressor = lgb.LGBMRegressor(**params)
    local_regressor.fit(X, y, sample_weight=w)
    p2 = local_regressor.predict(X)
    q2 = np.count_nonzero(y < p2) / y.shape[0]

    # Quantiles should be right
    np.testing.assert_allclose(q1, alpha, atol=0.2)
    np.testing.assert_allclose(q2, alpha, atol=0.2)

    # be sure LightGBM actually used at least one categorical column,
    # and that it was correctly treated as a categorical feature
    if output == 'dataframe-with-categorical':
        cat_cols = [
            col for col in dX.columns
            if dX.dtypes[col].name == 'category'
        ]
        tree_df = dask_regressor.booster_.trees_to_dataframe()
        node_uses_cat_col = tree_df['split_feature'].isin(cat_cols)
        assert node_uses_cat_col.sum() > 0
        assert tree_df.loc[node_uses_cat_col, "decision_type"].unique()[0] == '=='

    client.close(timeout=CLIENT_CLOSE_TIMEOUT)


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

    if output == 'dataframe-with-categorical':
        X, y, w, g, dX, dy, dw, dg = _create_ranking_data(
            output=output,
            group=group,
            n_features=1,
            n_informative=1
        )
    else:
        X, y, w, g, dX, dy, dw, dg = _create_ranking_data(
            output=output,
            group=group,
        )

    # rebalance small dask.array dataset for better performance.
    if output == 'array':
        dX = dX.persist()
        dy = dy.persist()
        dw = dw.persist()
        dg = dg.persist()
        _ = wait([dX, dy, dw, dg])
        client.rebalance()

    # 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
    }

    if output == 'dataframe-with-categorical':
        params["categorical_feature"] = [
            i for i, col in enumerate(dX.columns) if col.startswith('cat_')
        ]

    dask_ranker = lgb.DaskLGBMRanker(
        client=client,
        time_out=5,
        local_listen_port=listen_port,
        tree_learner_type='data_parallel',
        **params
    )
    dask_ranker = dask_ranker.fit(dX, dy, sample_weight=dw, group=dg)
    rnkvec_dask = dask_ranker.predict(dX)
    rnkvec_dask = rnkvec_dask.compute()
    rnkvec_dask_local = dask_ranker.to_local().predict(X)

    local_ranker = lgb.LGBMRanker(**params)
    local_ranker.fit(X, y, sample_weight=w, group=g)
    rnkvec_local = local_ranker.predict(X)

    # distributed ranker should be able to rank decently well and should
    # have high rank correlation with scores from serial ranker.
    dcor = spearmanr(rnkvec_dask, y).correlation
    assert dcor > 0.6
    assert spearmanr(rnkvec_dask, rnkvec_local).correlation > 0.8
    assert_eq(rnkvec_dask, rnkvec_dask_local)

    # be sure LightGBM actually used at least one categorical column,
    # and that it was correctly treated as a categorical feature
    if output == 'dataframe-with-categorical':
        cat_cols = [
            col for col in dX.columns
            if dX.dtypes[col].name == 'category'
        ]
        tree_df = dask_ranker.booster_.trees_to_dataframe()
        node_uses_cat_col = tree_df['split_feature'].isin(cat_cols)
        assert node_uses_cat_col.sum() > 0
        assert tree_df.loc[node_uses_cat_col, "decision_type"].unique()[0] == '=='

    client.close(timeout=CLIENT_CLOSE_TIMEOUT)


@pytest.mark.parametrize('task', ['classification', 'regression', 'ranking'])
def test_training_works_if_client_not_provided_or_set_after_construction(task, listen_port, client):
    if task == 'ranking':
        _, _, _, _, dX, dy, _, dg = _create_ranking_data(
            output='array',
            group=None
        )
        model_factory = lgb.DaskLGBMRanker
    else:
        _, _, _, dX, dy, _ = _create_data(
            objective=task,
            output='array',
        )
        dg = None
        if task == 'classification':
            model_factory = lgb.DaskLGBMClassifier
        elif task == 'regression':
            model_factory = lgb.DaskLGBMRegressor

    params = {
        "time_out": 5,
        "local_listen_port": listen_port,
        "n_estimators": 1,
        "num_leaves": 2
    }

    # should be able to use the class without specifying a client
    dask_model = model_factory(**params)
    assert dask_model.client is None
    with pytest.raises(lgb.compat.LGBMNotFittedError, match='Cannot access property client_ before calling fit'):
        dask_model.client_

    dask_model.fit(dX, dy, group=dg)
    assert dask_model.fitted_
    assert dask_model.client is None
    assert dask_model.client_ == client

    preds = dask_model.predict(dX)
    assert isinstance(preds, da.Array)
    assert dask_model.fitted_
    assert dask_model.client is None
    assert dask_model.client_ == client

    local_model = dask_model.to_local()
    with pytest.raises(AttributeError):
        local_model.client
        local_model.client_

    # should be able to set client after construction
    dask_model = model_factory(**params)
    dask_model.set_params(client=client)
    assert dask_model.client == client

    with pytest.raises(lgb.compat.LGBMNotFittedError, match='Cannot access property client_ before calling fit'):
        dask_model.client_

    dask_model.fit(dX, dy, group=dg)
    assert dask_model.fitted_
    assert dask_model.client == client
    assert dask_model.client_ == client

    preds = dask_model.predict(dX)
    assert isinstance(preds, da.Array)
    assert dask_model.fitted_
    assert dask_model.client == client
    assert dask_model.client_ == client

    local_model = dask_model.to_local()
    with pytest.raises(AttributeError):
        local_model.client
        local_model.client_

    client.close(timeout=CLIENT_CLOSE_TIMEOUT)


@pytest.mark.parametrize('serializer', ['pickle', 'joblib', 'cloudpickle'])
@pytest.mark.parametrize('task', ['classification', 'regression', 'ranking'])
@pytest.mark.parametrize('set_client', [True, False])
def test_model_and_local_version_are_picklable_whether_or_not_client_set_explicitly(serializer, task, set_client, listen_port, tmp_path):

    with LocalCluster(n_workers=2, threads_per_worker=1) as cluster1:
        with Client(cluster1) as client1:

            # data on cluster1
            if task == 'ranking':
                X_1, _, _, _, dX_1, dy_1, _, dg_1 = _create_ranking_data(
                    output='array',
                    group=None
                )
            else:
                X_1, _, _, dX_1, dy_1, _ = _create_data(
                    objective=task,
                    output='array',
                )
                dg_1 = None

            with LocalCluster(n_workers=2, threads_per_worker=1) as cluster2:
                with Client(cluster2) as client2:

                    # create identical data on cluster2
                    if task == 'ranking':
                        X_2, _, _, _, dX_2, dy_2, _, dg_2 = _create_ranking_data(
                            output='array',
                            group=None
                        )
                    else:
                        X_2, _, _, dX_2, dy_2, _ = _create_data(
                            objective=task,
                            output='array',
                        )
                        dg_2 = None

                    if task == 'ranking':
                        model_factory = lgb.DaskLGBMRanker
                    elif task == 'classification':
                        model_factory = lgb.DaskLGBMClassifier
                    elif task == 'regression':
                        model_factory = lgb.DaskLGBMRegressor

                    params = {
                        "time_out": 5,
                        "local_listen_port": listen_port,
                        "n_estimators": 1,
                        "num_leaves": 2
                    }

                    # at this point, the result of default_client() is client2 since it was the most recently
                    # created. So setting client to client1 here to test that you can select a non-default client
                    assert default_client() == client2
                    if set_client:
                        params.update({"client": client1})

                    # unfitted model should survive pickling round trip, and pickling
                    # shouldn't have side effects on the model object
                    dask_model = model_factory(**params)
                    local_model = dask_model.to_local()
                    if set_client:
                        assert dask_model.client == client1
                    else:
                        assert dask_model.client is None

                    with pytest.raises(lgb.compat.LGBMNotFittedError, match='Cannot access property client_ before calling fit'):
                        dask_model.client_

                    assert "client" not in local_model.get_params()
                    assert getattr(local_model, "client", None) is None

                    tmp_file = str(tmp_path / "model-1.pkl")
                    _pickle(
                        obj=dask_model,
                        filepath=tmp_file,
                        serializer=serializer
                    )
                    model_from_disk = _unpickle(
                        filepath=tmp_file,
                        serializer=serializer
                    )

                    local_tmp_file = str(tmp_path / "local-model-1.pkl")
                    _pickle(
                        obj=local_model,
                        filepath=local_tmp_file,
                        serializer=serializer
                    )
                    local_model_from_disk = _unpickle(
                        filepath=local_tmp_file,
                        serializer=serializer
                    )

                    assert model_from_disk.client is None

                    if set_client:
                        assert dask_model.client == client1
                    else:
                        assert dask_model.client is None

                    with pytest.raises(lgb.compat.LGBMNotFittedError, match='Cannot access property client_ before calling fit'):
                        dask_model.client_

                    # client will always be None after unpickling
                    if set_client:
                        from_disk_params = model_from_disk.get_params()
                        from_disk_params.pop("client", None)
                        dask_params = dask_model.get_params()
                        dask_params.pop("client", None)
                        assert from_disk_params == dask_params
                    else:
                        assert model_from_disk.get_params() == dask_model.get_params()
                    assert local_model_from_disk.get_params() == local_model.get_params()

                    # fitted model should survive pickling round trip, and pickling
                    # shouldn't have side effects on the model object
                    if set_client:
                        dask_model.fit(dX_1, dy_1, group=dg_1)
                    else:
                        dask_model.fit(dX_2, dy_2, group=dg_2)
                    local_model = dask_model.to_local()

                    assert "client" not in local_model.get_params()
                    with pytest.raises(AttributeError):
                        local_model.client
                        local_model.client_

                    tmp_file2 = str(tmp_path / "model-2.pkl")
                    _pickle(
                        obj=dask_model,
                        filepath=tmp_file2,
                        serializer=serializer
                    )
                    fitted_model_from_disk = _unpickle(
                        filepath=tmp_file2,
                        serializer=serializer
                    )

                    local_tmp_file2 = str(tmp_path / "local-model-2.pkl")
                    _pickle(
                        obj=local_model,
                        filepath=local_tmp_file2,
                        serializer=serializer
                    )
                    local_fitted_model_from_disk = _unpickle(
                        filepath=local_tmp_file2,
                        serializer=serializer
                    )

                    if set_client:
                        assert dask_model.client == client1
                        assert dask_model.client_ == client1
                    else:
                        assert dask_model.client is None
                        assert dask_model.client_ == default_client()
                        assert dask_model.client_ == client2

                    assert isinstance(fitted_model_from_disk, model_factory)
                    assert fitted_model_from_disk.client is None
                    assert fitted_model_from_disk.client_ == default_client()
                    assert fitted_model_from_disk.client_ == client2

                    # client will always be None after unpickling
                    if set_client:
                        from_disk_params = fitted_model_from_disk.get_params()
                        from_disk_params.pop("client", None)
                        dask_params = dask_model.get_params()
                        dask_params.pop("client", None)
                        assert from_disk_params == dask_params
                    else:
                        assert fitted_model_from_disk.get_params() == dask_model.get_params()
                    assert local_fitted_model_from_disk.get_params() == local_model.get_params()

                    if set_client:
                        preds_orig = dask_model.predict(dX_1).compute()
                        preds_loaded_model = fitted_model_from_disk.predict(dX_1).compute()
                        preds_orig_local = local_model.predict(X_1)
                        preds_loaded_model_local = local_fitted_model_from_disk.predict(X_1)
                    else:
                        preds_orig = dask_model.predict(dX_2).compute()
                        preds_loaded_model = fitted_model_from_disk.predict(dX_2).compute()
                        preds_orig_local = local_model.predict(X_2)
                        preds_loaded_model_local = local_fitted_model_from_disk.predict(X_2)

                    assert_eq(preds_orig, preds_loaded_model)
                    assert_eq(preds_orig_local, preds_loaded_model_local)


def test_find_open_port_works():
    worker_ip = '127.0.0.1'
    with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
        s.bind((worker_ip, 12400))
        new_port = lgb.dask._find_open_port(
            worker_ip=worker_ip,
            local_listen_port=12400,
            ports_to_skip=set()
        )
        assert new_port == 12401

    with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s_1:
        s_1.bind((worker_ip, 12400))
        with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s_2:
            s_2.bind((worker_ip, 12401))
            new_port = lgb.dask._find_open_port(
                worker_ip=worker_ip,
                local_listen_port=12400,
                ports_to_skip=set()
            )
            assert new_port == 12402


def test_warns_and_continues_on_unrecognized_tree_learner(client):
    X = da.random.random((1e3, 10))
    y = da.random.random((1e3, 1))
    dask_regressor = lgb.DaskLGBMRegressor(
        client=client,
        time_out=5,
        local_listen_port=1234,
        tree_learner='some-nonsense-value',
        n_estimators=1,
        num_leaves=2
    )
    with pytest.warns(UserWarning, match='Parameter tree_learner set to some-nonsense-value'):
        dask_regressor = dask_regressor.fit(X, y)

    assert dask_regressor.fitted_

    client.close(timeout=CLIENT_CLOSE_TIMEOUT)


def test_warns_but_makes_no_changes_for_feature_or_voting_tree_learner(client):
    X = da.random.random((1e3, 10))
    y = da.random.random((1e3, 1))
    for tree_learner in ['feature_parallel', 'voting']:
        dask_regressor = lgb.DaskLGBMRegressor(
            client=client,
            time_out=5,
            local_listen_port=1234,
            tree_learner=tree_learner,
            n_estimators=1,
            num_leaves=2
        )
        with pytest.warns(UserWarning, match='Support for tree_learner %s in lightgbm' % tree_learner):
            dask_regressor = dask_regressor.fit(X, y)

        assert dask_regressor.fitted_
        assert dask_regressor.get_params()['tree_learner'] == tree_learner

    client.close(timeout=CLIENT_CLOSE_TIMEOUT)


@gen_cluster(client=True, timeout=None)
def test_errors(c, s, a, b):
    def f(part):
        raise Exception('foo')

    df = dd.demo.make_timeseries()
    df = df.map_partitions(f, meta=df._meta)
    with pytest.raises(Exception) as info:
        yield lgb.dask._train(
            client=c,
            data=df,
            label=df.x,
            params={},
            model_factory=lgb.LGBMClassifier
        )
        assert 'foo' in str(info.value)


@pytest.mark.parametrize(
    "classes",
    [
        (lgb.DaskLGBMClassifier, lgb.LGBMClassifier),
        (lgb.DaskLGBMRegressor, lgb.LGBMRegressor),
        (lgb.DaskLGBMRanker, lgb.LGBMRanker)
    ]
)
def test_dask_classes_and_sklearn_equivalents_have_identical_constructors_except_client_arg(classes):
    dask_spec = inspect.getfullargspec(classes[0])
    sklearn_spec = inspect.getfullargspec(classes[1])
    assert dask_spec.varargs == sklearn_spec.varargs
    assert dask_spec.varkw == sklearn_spec.varkw
    assert dask_spec.kwonlyargs == sklearn_spec.kwonlyargs
    assert dask_spec.kwonlydefaults == sklearn_spec.kwonlydefaults

    # "client" should be the only different, and the final argument
    assert dask_spec.args[:-1] == sklearn_spec.args
    assert dask_spec.defaults[:-1] == sklearn_spec.defaults
    assert dask_spec.args[-1] == 'client'
    assert dask_spec.defaults[-1] is None