Add catboost config and notebook

examples/benchmarks/CatBoost/workflow_config_catboost.yaml

@@ -0,0 +1,53 @@
+provider_uri: "~/.qlib/qlib_data/cn_data"
+market: &market csi300
+benchmark: &benchmark SH000300
+data_handler_config: &data_handler_config
+    start_time: 2008-01-01
+    end_time: 2020-08-01
+    fit_start_time: 2008-01-01
+    fit_end_time: 2014-12-31
+    instruments: *market
+port_analysis_config: &port_analysis_config
+    strategy:
+        class: TopkDropoutStrategy
+        module_path: qlib.contrib.strategy.strategy
+        kwargs:
+            topk: 50
+            n_drop: 5
+    backtest:
+        verbose: False
+        limit_threshold: 0.095
+        account: 100000000
+        benchmark: *benchmark
+        deal_price: close
+        open_cost: 0.0005
+        close_cost: 0.0015
+        min_cost: 5
+task:
+    model:
+        class: CatBoostModel
+        module_path: qlib.contrib.model.catboost_model
+        kwargs:
+            loss: RMSE
+            iterations: 5
+            learning_rate: 0.03
+    dataset:
+        class: DatasetH
+        module_path: qlib.data.dataset
+        kwargs:
+            handler:
+                class: ALPHA360_Denoise
+                module_path: qlib.contrib.data.handler
+                kwargs: *data_handler_config
+            segments:
+                train: [2008-01-01, 2014-12-31]
+                valid: [2015-01-01, 2016-12-31]
+                test: [2017-01-01, 2020-08-01]
+    record: 
+        - class: SignalRecord
+          module_path: qlib.workflow.record_temp
+          kwargs: {}
+        - class: PortAnaRecord
+          module_path: qlib.workflow.record_temp
+          kwargs: 
+            config: *port_analysis_config

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)