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FIX-#3767: Fix tests for Modin XGB Dmatrix #3770

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merged 4 commits into from
Dec 3, 2021
+27 −43
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FIX-#3767: Fix tests for Modin XGB Dmatrix #3770

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706083e
FIX-#3767: Fix tests for Modin XGB Dmatrix
prutskov Dec 2, 2021
ba1ab58
Address review comments
prutskov Dec 2, 2021
9531767
Apply comments, remove omission
prutskov Dec 2, 2021
5033939
Use pandas df instead modin
prutskov Dec 2, 2021
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70 changes: 27 additions & 43 deletions modin/experimental/xgboost/test/test_dmatrix.py
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Original file line number Diff line number Diff line change
Expand Up @@ -13,7 +13,9 @@

import numpy as np
import pytest
import pandas
from sklearn.metrics import accuracy_score
from sklearn.datasets import load_breast_cancer
import xgboost as xgb

import modin.pandas as pd
Expand Down Expand Up @@ -74,67 +76,49 @@ def test_dmatrix_feature_names_and_feature_types(data, feature_names, feature_ty
check_dmatrix(data, feature_names=feature_names, feature_types=feature_types)


@pytest.mark.parametrize(
"feature_names",
[
["Feature1", "Feature2", "Feature3", "Feature4", "Feature5"],
[u"??1", u"??2", u"??3", u"??4", u"??5"],
],
)
def test_feature_names(feature_names):
data = np.random.randn(100, 5)
label = np.array([0, 1] * 50)
def test_feature_names():
dataset = load_breast_cancer()
X = dataset.data
y = dataset.target
feature_names = [f"feat{i}" for i in range(X.shape[1])]

check_dmatrix(
data,
label,
X,
y,
feature_names=feature_names,
)

dm = xgb.DMatrix(data, label=label, feature_names=feature_names)
md_dm = mxgb.DMatrix(
pd.DataFrame(data), label=pd.Series(label), feature_names=feature_names
dmatrix = xgb.DMatrix(X, label=y, feature_names=feature_names)
md_dmatrix = mxgb.DMatrix(
pd.DataFrame(X), label=pd.Series(y), feature_names=feature_names
)

params = {
"objective": "multi:softprob",
"objective": "binary:logistic",
"eval_metric": "mlogloss",
"eta": 0.3,
"num_class": 3,
}

bst = xgb.train(params, dm, num_boost_round=10)
md_bst = mxgb.train(params, md_dm, num_boost_round=10)
booster = xgb.train(params, dmatrix, num_boost_round=10)
md_booster = mxgb.train(params, md_dmatrix, num_boost_round=10)

predictions = bst.predict(dm)
modin_predictions = md_bst.predict(md_dm)
predictions = booster.predict(dmatrix)
modin_predictions = md_booster.predict(md_dmatrix)

preds = np.asarray([np.argmax(line) for line in predictions])
md_preds = np.asarray([np.argmax(line) for line in modin_predictions.to_numpy()])
preds = pandas.DataFrame(predictions).apply(np.round, axis=0)
modin_preds = modin_predictions.apply(np.round, axis=0)

val = accuracy_score(label, preds)
md_val = accuracy_score(label, md_preds)
accuracy = accuracy_score(y, preds)
md_accuracy = accuracy_score(y, modin_preds)

np.testing.assert_allclose(val, md_val, atol=0.02, rtol=0.01)
np.testing.assert_allclose(accuracy, md_accuracy, atol=0.005, rtol=0.002)

dummy = np.random.randn(5, 5)
dm = xgb.DMatrix(dummy, feature_names=feature_names)
md_dm = mxgb.DMatrix(pd.DataFrame(dummy), feature_names=feature_names)
predictions = bst.predict(dm)
modin_predictions = md_bst.predict(md_dm)

preds = np.asarray([np.argmax(line) for line in predictions])
md_preds = np.asarray([np.argmax(line) for line in modin_predictions.to_numpy()])

assert preds.all() == md_preds.all()

# different feature names must raises error
dm = xgb.DMatrix(dummy, feature_names=list("abcde"))
md_dm = mxgb.DMatrix(pd.DataFrame(dummy), feature_names=list("abcde"))
# Different feature_names (default) must raise error in this case
dm = xgb.DMatrix(X)
md_dm = mxgb.DMatrix(pd.DataFrame(X))
with pytest.raises(ValueError):
bst.predict(dm)
booster.predict(dm)
with pytest.raises(ValueError):
md_bst.predict(md_dm)
repr(md_booster.predict(md_dm))


def test_feature_weights():
Expand Down