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[doc] Small improvements for categorical data document. (#7330)

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This commit is contained in:
Jiaming Yuan
2021-10-20 18:04:32 +08:00
committed by GitHub
parent f999897615
commit 15685996fc
3 changed files with 19 additions and 16 deletions
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12
demo/guide-python/cat_in_the_dat.py
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@@ -7,6 +7,9 @@ https://www.kaggle.com/shahules/an-overview-of-encoding-techniques
And the data can be found at:
https://www.kaggle.com/shahules/an-overview-of-encoding-techniques/data
Also, see the tutorial for using XGBoost with categorical data:
https://xgboost.readthedocs.io/en/latest/tutorials/categorical.html
.. versionadded 1.6.0
"""
@@ -48,8 +51,6 @@ def load_cat_in_the_dat() -> tuple[pd.DataFrame, pd.Series]:
for i in range(0, 6):
X["ord_" + str(i)] = X["ord_" + str(i)].astype("category")
print(X.shape)
print(
"train data set has got {} rows and {} columns".format(X.shape[0], X.shape[1])
)
@@ -64,7 +65,7 @@ def categorical_model(X: pd.DataFrame, y: pd.Series, output_dir: str) -> None:
X_train, X_test, y_train, y_test = train_test_split(
X, y, random_state=1994, test_size=0.2
)
# Specify `enable_categorical`.
clf = xgb.XGBClassifier(**params, enable_categorical=True)
clf.fit(
X_train,
@@ -72,7 +73,6 @@ def categorical_model(X: pd.DataFrame, y: pd.Series, output_dir: str) -> None:
eval_set=[(X_test, y_test), (X_train, y_train)],
eval_metric="auc",
)
print(clf.n_classes_)
clf.save_model(os.path.join(output_dir, "categorical.json"))
y_score = clf.predict_proba(X_test)[:, 1] # proba of positive samples
@@ -82,12 +82,10 @@ def categorical_model(X: pd.DataFrame, y: pd.Series, output_dir: str) -> None:
def onehot_encoding_model(X: pd.DataFrame, y: pd.Series, output_dir: str) -> None:
"""Train using one-hot encoded data."""
X_train, X_test, y_train, y_test = train_test_split(
X, y, random_state=42, test_size=0.2
)
print(X_train.shape, y_train.shape)
# Specify `enable_categorical`.
clf = xgb.XGBClassifier(**params, enable_categorical=False)
clf.fit(
X_train,

3
demo/guide-python/categorical.py
View File

@@ -5,6 +5,9 @@ In before, users need to run an encoder themselves before passing the data into
which creates a sparse matrix and potentially increase memory usage. This demo showcases
the experimental categorical data support, more advanced features are planned.
Also, see the tutorial for using XGBoost with categorical data:
https://xgboost.readthedocs.io/en/latest/tutorials/categorical.html
.. versionadded:: 1.5.0
"""