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use gain for sklearn feature_importances_ (#3876)

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* use gain for sklearn feature_importances_

`gain` is a better feature importance criteria than the currently used `weight`

* added importance_type to class

* fixed test

* white space

* fix variable name

* fix deprecation warning

* fix exp array

* white spaces
This commit is contained in:
Dr. Kashif Rasul
2018-11-13 12:30:40 +01:00
committed by Philip Hyunsu Cho
parent 926eb651fe
commit 143475b27b
2 changed files with 41 additions and 7 deletions
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37
tests/python/test_with_sklearn.py
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@@ -104,14 +104,14 @@ def test_ranking():
np.testing.assert_almost_equal(pred, pred_orig)
def test_feature_importances():
def test_feature_importances_weight():
tm._skip_if_no_sklearn()
from sklearn.datasets import load_digits
digits = load_digits(2)
y = digits['target']
X = digits['data']
xgb_model = xgb.XGBClassifier(seed=0).fit(X, y)
xgb_model = xgb.XGBClassifier(random_state=0, importance_type="weight").fit(X, y)
exp = np.array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.00833333, 0.,
0., 0., 0., 0., 0., 0., 0., 0.025, 0.14166667, 0., 0., 0.,
@@ -127,10 +127,39 @@ def test_feature_importances():
import pandas as pd
y = pd.Series(digits['target'])
X = pd.DataFrame(digits['data'])
xgb_model = xgb.XGBClassifier(seed=0).fit(X, y)
xgb_model = xgb.XGBClassifier(random_state=0, importance_type="weight").fit(X, y)
np.testing.assert_almost_equal(xgb_model.feature_importances_, exp)
xgb_model = xgb.XGBClassifier(seed=0).fit(X, y)
xgb_model = xgb.XGBClassifier(random_state=0, importance_type="weight").fit(X, y)
np.testing.assert_almost_equal(xgb_model.feature_importances_, exp)
def test_feature_importances_gain():
tm._skip_if_no_sklearn()
from sklearn.datasets import load_digits
digits = load_digits(2)
y = digits['target']
X = digits['data']
xgb_model = xgb.XGBClassifier(random_state=0, importance_type="gain").fit(X, y)
exp = np.array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.00326159, 0., 0., 0.,
0., 0., 0., 0., 0., 0.00297238, 0.00988034, 0., 0., 0., 0.,
0., 0., 0.03512521, 0.41123885, 0., 0., 0., 0., 0.01326332,
0.00160674, 0., 0.4206952, 0., 0., 0., 0., 0.00616747, 0.01237546,
0., 0., 0., 0., 0., 0., 0., 0.08240705, 0., 0., 0., 0.,
0., 0., 0., 0.00100649, 0., 0., 0., 0., 0.], dtype=np.float32)
np.testing.assert_almost_equal(xgb_model.feature_importances_, exp)
# numeric columns
import pandas as pd
y = pd.Series(digits['target'])
X = pd.DataFrame(digits['data'])
xgb_model = xgb.XGBClassifier(random_state=0, importance_type="gain").fit(X, y)
np.testing.assert_almost_equal(xgb_model.feature_importances_, exp)
xgb_model = xgb.XGBClassifier(random_state=0, importance_type="gain").fit(X, y)
np.testing.assert_almost_equal(xgb_model.feature_importances_, exp)