diff --git a/test.py b/test.py
deleted file mode 100644
index 85d49e5c2..000000000
--- a/test.py
+++ /dev/null
@@ -1,57 +0,0 @@
-import pytest
-from python.xorbits import numpy as np
-from python.xorbits import pandas as pd
-from python.xorbits import xgboost as xxgb
-from python.xorbits._mars.core.entity.objects import ObjectData
-
-
-X = np.random.rand(100, 10)
-X_df = pd.DataFrame(X)
-y = np.random.randint(0, 2, 100)
-
-classifier = xxgb.XGBClassifier(verbosity=1, n_estimators=2)
-
-classifier.fit(X_df, y, eval_set=[(X_df, y)])
-pred = classifier.predict(X_df)
-
-assert pred.ndim == 1
-assert pred.shape[0] == len(X_df)
-
-history = classifier.evals_result()
-
-assert isinstance(history, dict)
-
-assert list(history)[0] == "validation_0"
-
-prob = classifier.predict_proba(X_df)
-
-assert prob.shape[0] == X_df.shape[0]
-
-assert len(pred) == len(y)
-assert set(pred.to_numpy().to_numpy()).issubset({0, 1})
-
-# test weight
-weights = [
-    np.random.rand(X_df.shape[0]),
-    pd.Series(np.random.rand(X_df.shape[0])),
-    pd.DataFrame(np.random.rand(X_df.shape[0])),
-]
-y_df = pd.DataFrame(y)
-for weight in weights:
-    classifier.fit(X_df, y_df, sample_weight=weight)
-    prediction = classifier.predict(X_df)
-
-    assert prediction.ndim == 1
-    assert prediction.shape[0] == len(X_df)
-
-# should raise error if weight.ndim > 1
-with pytest.raises(ValueError):
-    classifier.fit(X_df, y_df, sample_weight=np.random.rand(1, 1))
-
-# test wrong argument
-with pytest.raises(TypeError):
-    classifier.fit(X_df, y, wrong_param=1)
-
-# test wrong attribute
-with pytest.raises(AttributeError):
-    classifier.wrong_attribute()