Add catboost config and notebook

examples/benchmarks/XGBoost/xgboost.py

@@ -1,64 +0,0 @@
-# Copyright (c) Microsoft Corporation.
-# Licensed under the MIT License.
-
-import numpy as np
-import pandas as pd
-import xgboost as xgb
-
-from ...model.base import Model
-from ...data.dataset import DatasetH
-from ...data.dataset.handler import DataHandlerLP
-
-
-class XGBModel(Model):
-    """XGBModel Model"""
-
-    def __init__(self, obj="mse", **kwargs):
-        if obj not in {"mse", "binary"}:
-            raise NotImplementedError
-        self._params = {"obj": obj}
-        self._params.update(kwargs)
-        self.model = None
-
-    def fit(
-        self,
-        dataset: DatasetH,
-        num_boost_round=1000,
-        early_stopping_rounds=50,
-        verbose_eval=20,
-        evals_result=dict(),
-        **kwargs
-    ):
-
-        df_train, df_valid = dataset.prepare(
-            ["train", "valid"], col_set=["feature", "label"], data_key=DataHandlerLP.DK_L
-        )
-        x_train, y_train = df_train["feature"], df_train["label"]
-        x_valid, y_valid = df_valid["feature"], df_valid["label"]
-
-        # Lightgbm need 1D array as its label
-        if y_train.values.ndim == 2 and y_train.values.shape[1] == 1:
-            y_train_1d, y_valid_1d = np.squeeze(y_train.values), np.squeeze(y_valid.values)
-        else:
-            raise ValueError("XGBoost doesn't support multi-label training")
-
-        dtrain = xgb.DMatrix(x_train.values, label=y_train_1d)
-        dvalid = xgb.DMatrix(x_valid.values, label=y_valid_1d)
-        self.model = xgb.train(
-            self._params,
-            dtrain=dtrain,
-            num_boost_round=num_boost_round,
-            evals=[(dtrain, "train"), (dvalid, "valid")],
-            early_stopping_rounds=early_stopping_rounds,
-            verbose_eval=verbose_eval,
-            evals_result=evals_result,
-            **kwargs
-        )
-        evals_result["train"] = list(evals_result["train"].values())[0]
-        evals_result["valid"] = list(evals_result["valid"].values())[0]
-
-    def predict(self, dataset):
-        if self.model is None:
-            raise ValueError("model is not fitted yet!")
-        x_test = dataset.prepare("test", col_set="feature")
-        return pd.Series(self.model.predict(xgb.DMatrix(np.squeeze(x_test.values))), index=x_test.index)